Name: Data Science & Machine Learning with R
Price: 12 GBP
Availability: InStock

<p>Explore the fundamental ideas of Data Science &amp; Machine Learning using R in a detailed course. This covers important areas like handling data, creating visuals, making reports, developing web apps, and getting introduced to machine learning. Acquire the essential skills to kickstart a thriving career in the world of Data Science and Machine Learning.</p><p><strong>Key Takeaways:</strong></p><ul><li>Grasp the basics of Data Science and Machine Learning using R.</li><li>Analyse and manipulate data using various data types and structures in R.</li><li>Make attractive visualizations to understand complex datasets better.</li><li>Become skilled at crafting reports using R Markdown for effective data communication.</li><li>Gain proficiency in creating reports using R Markdown for efficient data communication.</li><li>Explore the basics of Machine Learning and its applications in solving real-world problems.</li><li>Develop the knowledge and confidence needed to pursue a fulfilling career in Data Science.</li></ul><p><strong>Our Service for the </strong><strong>Data Science &amp; Machine Learning with R</strong><strong> Includes: </strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Assignment Help</li><li>QLS Endorsed Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Study Materials</li></ul><p><strong>Course Curriculum of </strong><strong>Data Science &amp; Machine Learning with R</strong></p><ul><li>Data Science and Machine Learning Course Intro</li><li>Getting Started with R</li><li>Data Types and Structures in R</li><li>Intermediate R</li><li>Data Manipulation in R</li><li>Data Visualization in R</li><li>Creating Reports with R Markdown</li><li>Building Webapps with R Shiny</li><li>Introduction to Machine Learning</li><li>Starting A Career in Data Science</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Students need to finish and turn in a thorough assignment covering all topics in our curriculum. Our expert tutor will review the assignment to make sure it meets Quality Learning Systems (QLS) standards. Once the assessment is successful and passes a quality check, students will be awarded a certificate for completing the Data Science &amp; Machine Learning with R.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme</strong></p><p>After the course, participants will receive an endorsed certificate as proof of successful completion. The learner may request a certificate if they complete all course assessments.</p><p><strong>Endorsement:</strong></p><p>This certified course is approved by QLS as a well-qualified, non-regulated training program. A trainer will be available to address your inquiries, including guidance on your pathways to higher education.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        20 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Individuals interested in entering the field of Data Science and Machine Learning.</li><li>Professionals looking to enhance their data manipulation and analysis skills using R.</li><li>Graduates seeking to gain a competitive edge in the job market with specialized knowledge in Data Science.</li><li>Those who want to explore the applications of Machine Learning and its impact on various industries.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no specific requirements for enrolling in the Data Science &amp; Machine Learning with Rcourse. Therefore, learners do not need previous qualifications to sign up for this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £25,000 - £45,000 per annum</li><li>Machine Learning Engineer: £35,000 - £65,000 per annum</li><li>Data Scientist: £40,000 - £70,000 per annum</li><li>Business Intelligence Analyst: £30,000 - £55,000 per annum</li><li>Data Engineer: £35,000 - £60,000 per annum</li><li>AI Research Scientist: £50,000 - £90,000 per annum</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4> Advanced Diploma in Data Science &amp; Machine Learning with R at QLS Level 7</h4>
        <p>
            Hard copy certificate -
            £75
        </p>

                        </div>

Kingston Open College

Learning & Development

Learning Support Assistant (LSA)

Data Engineering

Data Science

Diploma in Management Studies (DMS)

Data Skills

Level 7 QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support

Diploma in Data Science & Machine Learning with R - Level 7 (QLS Endorsed)

General Admission

<strong>This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.</strong>
        <p>Welcome to the course where you will learn about the NumPy stack in Python, which is an important prerequisite for deep learning, machine learning, and data science.

In this self-paced course, you will learn how to use NumPy, Matplotlib, Pandas, and SciPy to perform critical tasks related to data science and machine learning. This involves performing numerical computation and representing data, visualizing data with plots, loading in, and manipulating data using DataFrames, performing statistics and probability, and building machine learning models for classification and regression.

In this course, we will first start with NumPy; we will understand the benefits of NumPy array and then we will look at some complicated matrix operations, such as products, inverses, determinants, and solving linear systems.

Then we will cover Matplotlib. In this section, we will go over some common plots, namely the line chart, scatter plot, and histogram. We will also look at how to show images using Matplotlib.

Next, we will talk about Pandas. We will look at how much easier it is to load a dataset using Pandas versus trying to do it manually. Then we will look at some data frame operations useful in machine learning, such as filtering by column, filtering by row, and the apply function.

Later, you will learn about SciPy. In this section, you will learn how to do common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing.

Finally, we will also cover some basics of machine learning that will help us start our deep learning journey.

By the end of the course, we will be able to confidently use the NumPy stack in deep learning and data science.</p>
        <h2>What You Will Learn</h2><p>Understand supervised machine learning with real-world examples<br>Understand and code using the NumPy stack<br>Make use of NumPy, SciPy, Matplotlib, and Pandas to implement numerical algorithms<br>Understand the pros and cons of various machine learning models<br>Get a brief introduction to the classification and regression<br>Learn how to calculate the PDF and CDF under the normal distribution</p>
        <h2>Audience</h2><p>This course is designed for anyone who is interested in data science and machine learning, who knows Python and wants to take the next step into Python libraries for data science, or who is interested in acquiring tools to implement machine learning algorithms.<br><br>One must have decent Python programming skills and a basic understanding of linear algebra and probability for this course.</p>
        <h2>Approach</h2><p>You will learn how to use NumPy, Matplotlib, Pandas, and SciPy to carry out crucial data science and machine learning tasks in this self-paced course. The course is well-balanced with both theoretical and practical coding exercises. Each section, we first cover the theory concept and demonstrate it using a real-world example for better understanding.</p>
        <h2>Key Features</h2><p>Study basics of machine learning and understand how to use the NumPy stack for deep learning in data science * Learn how to use NumPy, Matplotlib, Pandas, and SciPy for critical tasks in data science and machine learning * Perform numerical computations, visualize data, load, and manipulate datasets using Pandas</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer is an AI/ML engineer focusing on deep learning with experience in data science, big data engineering, and full-stack development. With a background in computer engineering and specialization in ML, he holds two master's degrees in computer engineering and statistics with finance applications. His online advertising and digital media expertise include data science and big data.

He has created DL models for prediction and has experience in recommender systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He is a web programmer with experience in Python, Ruby/Rails, PHP, and Angular.</p>
        <h2>Course Outline</h2><h4>1. Welcome and Logistics</h4><p>Welcome to the course! In this section, we will get introduced to the course goals.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction and Outline</p><p style='margin: 0'>In this video, we will get introduced to the course and understand the learning objective.</p></td></tr></tbody></table><br><h4>2. NumPy</h4><p>In this section of the course, we will dive into the world of NumPy, a powerful numerical computing library in Python that is widely used in data science and machine learning.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. NumPy Section Introduction</p><p style='margin: 0'>In this video, we will have a quick overview of NumPy.</p></td></tr><tr><td style='padding: 15px;'><p>2. Arrays Versus Lists</p><p style='margin: 0'>In this video, we will be comparing NumPy array to Python list.</p></td></tr><tr><td style='padding: 15px;'><p>3. Dot Product</p><p style='margin: 0'>In this video, we will discuss Dot Product in NumPy.</p></td></tr><tr><td style='padding: 15px;'><p>4. Speed Test</p><p style='margin: 0'>In this video, we will do a speed test to measure how much faster NumPy is than regular Python list in terms of the dot product.</p></td></tr><tr><td style='padding: 15px;'><p>5. Matrices</p><p style='margin: 0'>In this video, you will learn about matrices.</p></td></tr><tr><td style='padding: 15px;'><p>6. Solving Linear Systems</p><p style='margin: 0'>In this video, we will work on solving an example problem to understand linear systems.</p></td></tr><tr><td style='padding: 15px;'><p>7. Generating Data</p><p style='margin: 0'>In this video, we will discuss about generatingabout how to generate data.</p></td></tr><tr><td style='padding: 15px;'><p>8. NumPy Exercise</p><p style='margin: 0'>In this video, we will look at the NumPy exercise that must be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>9. Where to Learn More NumPy</p><p style='margin: 0'>In this video, we will understand why we need NumPy and how it is applied in more advanced scenarios.</p></td></tr></tbody></table><br><h4>3. Matplotlib</h4><p>In this section, we will talk about Matplotlib.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Matplotlib Section Introduction</p><p style='margin: 0'>In this video, we will have a quick overview of Matplotlib that is used to visualize data and see what we are going to cover in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Line Chart</p><p style='margin: 0'>In this video, you will learn about a line chart.</p></td></tr><tr><td style='padding: 15px;'><p>3. Scatterplot</p><p style='margin: 0'>In this video, you will learn about a scatterplot.</p></td></tr><tr><td style='padding: 15px;'><p>4. Histogram</p><p style='margin: 0'>In this video, you will learn about a histogram.</p></td></tr><tr><td style='padding: 15px;'><p>5. Plotting Images</p><p style='margin: 0'>In this video, you will learn how to plot images.</p></td></tr><tr><td style='padding: 15px;'><p>6. Matplotlib Exercise</p><p style='margin: 0'>In this video, we will have a look at the Matplotlib exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>7. Where to Learn More Matplotlib</p><p style='margin: 0'>In this video, we will understand from where we can learn more about Matplotlib.</p></td></tr></tbody></table><br><h4>4. Pandas</h4><p>In this section, we will talk about Pandas.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Pandas Section Introduction</p><p style='margin: 0'>In this video, we will be introduced to Pandas and understand this section''s learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Loading in Data</p><p style='margin: 0'>In this video, you will learn how to load in data using Pandas.</p></td></tr><tr><td style='padding: 15px;'><p>3. Selecting Rows and Columns</p><p style='margin: 0'>In this video, you will learn how to select rows and columns from a data frame.</p></td></tr><tr><td style='padding: 15px;'><p>4. The apply() Function</p><p style='margin: 0'>In this video, you will learn about the apply() function.</p></td></tr><tr><td style='padding: 15px;'><p>5. Plotting with Pandas</p><p style='margin: 0'>In this video, you will learn how to plot with Pandas.</p></td></tr><tr><td style='padding: 15px;'><p>6. Pandas Exercise</p><p style='margin: 0'>In this video, we will have a look at the Pandas exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>7. Where to Learn More Pandas</p><p style='margin: 0'>In this video, we will understand from where we can learn more about Pandas.</p></td></tr></tbody></table><br><h4>5. SciPy</h4><p>In this section, we will talk about SciPy.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. SciPy Section Introduction</p><p style='margin: 0'>In this video, we will be introduced to SciPy and understand this section''s learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. PDF and CDF</p><p style='margin: 0'>In this video, you will learn how to calculate PDF and CDF under normal distribution.</p></td></tr><tr><td style='padding: 15px;'><p>3. Convolution</p><p style='margin: 0'>In this video, you will learn how to apply convolution to an image to create a blurring filter.</p></td></tr><tr><td style='padding: 15px;'><p>4. SciPy Exercise</p><p style='margin: 0'>In this video, we will have a look at the SciPy exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>5. Where to Learn More SciPy</p><p style='margin: 0'>In this video, we will summarize what we have learnt about SciPy and understand where it can be applied.</p></td></tr></tbody></table><br><h4>6. Machine Learning Basics</h4><p>In this section, we will cover machine learning basics.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Machine Learning: Section Introduction</p><p style='margin: 0'>In this video, we will understand what machine learning is and the learning objective of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Is Classification?</p><p style='margin: 0'>In this video, we will understand classification.</p></td></tr><tr><td style='padding: 15px;'><p>3. Classification in Code</p><p style='margin: 0'>In this video, we will practice all that we have learnt in the previous video about classification.</p></td></tr><tr><td style='padding: 15px;'><p>4. What Is Regression?</p><p style='margin: 0'>In this video, we will understand what regression is.</p></td></tr><tr><td style='padding: 15px;'><p>5. Regression in Code</p><p style='margin: 0'>In this video, we will practice all that we have learnt in the previous video about regression.</p></td></tr><tr><td style='padding: 15px;'><p>6. What Is a Feature Vector?</p><p style='margin: 0'>In this video, you will learn about a feature vector.</p></td></tr><tr><td style='padding: 15px;'><p>7. Machine Learning Is Nothing but Geometry.</p><p style='margin: 0'>In this video, we will understand that machine learning is nothing but a geometry problem and see how it works for classification and regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. All Data Is the Same</p><p style='margin: 0'>In this video, we will understand how all data is the same as all machine learning interfaces are the same.</p></td></tr><tr><td style='padding: 15px;'><p>9. Comparing Different Machine Learning Models</p><p style='margin: 0'>In this video, we will understand how all machine learning interfaces are the same.</p></td></tr><tr><td style='padding: 15px;'><p>10. Machine Learning and Deep Learning: Future Topics</p><p style='margin: 0'>In this video, you will learn what other types of machine learning exist and some future topics that we can look forward to learning.</p></td></tr><tr><td style='padding: 15px;'><p>11. Machine Learning: Section Summary</p><p style='margin: 0'>In this video, we will summarize our learning from this section.</p></td></tr></tbody></table>

Packt

Python

Pandas

Deep Learning

This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.

Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python

<p>Are you interested in learning one of the most popular and widely-used programming languages in the world? Look no further than our Python (Machine Learning &amp; Programming) bundle, endorsed by the Quality Licence Scheme. This comprehensive bundle is designed to provide you with a solid foundation in Python programming and machine learning, allowing you to pursue a range of exciting career paths in this rapidly-growing field.</p> <p>The Python programming course covers the fundamental concepts of programming in Python, including variables, data types, functions, and control structures. You'll also learn about object-oriented programming, file handling, and exception handling, giving you a broad understanding of how to use Python to solve real-world problems.</p> <p>In addition to Python programming, this bundle also covers machine learning, one of the most in-demand skills in the tech industry. The Machine Learning course provides a comprehensive overview of the concepts and techniques used in machine learning, including regression analysis, decision trees, and clustering. You'll also learn about data pre-processing, model selection, and evaluation, giving you the skills and knowledge needed to build and deploy machine learning models.</p> <p>Upon completing this bundle, you'll receive a hardcopy certificate endorsed by the Quality Licence Scheme, proving your Python programming and machine learning proficiency. So why wait? Enrol today and take the first step towards a rewarding and challenging career in the tech industry.</p> <p><strong>This bundle comprises the following courses:</strong></p> <p><strong>QLS Endorsed Courses:</strong></p> <ul> <li><strong>Course 01:</strong> Certificate in Machine Learning with Python at QLS Level 3</li><li><strong>Course 02:</strong> Diploma in Python Programming at QLS Level 5</li></ul> <p><strong>CPD QS Accredited Courses:</strong></p> <ul> <li><strong>Course 03:</strong> Azure Machine Learning</li><li><strong>Course 04:</strong> Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 05:</strong> Machine Learning Model Using AWS SageMaker Canvas</li><li><strong>Course 06:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>Course 07:</strong> 2021 Data Science &amp; Machine Learning with R from A-Z</li></ul> <p><strong>Learning Outcomes</strong></p> <ul> <li>Develop a solid understanding of Python for building applications and data manipulation.</li><li>Learn the fundamental concepts and techniques used in machine learning and data science using Python.</li><li>Acquire the skills to build and train machine learning models for predictive analysis.</li><li>Understand how to use popular tools and platforms like AWS SageMaker and Azure for machine learning.</li><li>Develop a good understanding of statistics and probability concepts used in data science and machine learning.</li><li>Gain hands-on experience in using R programming language for data science and machine learning.</li><li>Acquire a valuable skill set that can be applied to various industries such as finance, healthcare, and tech.</li></ul> <p><strong>Please Note:</strong> Imperial Academy is a One Education approved resale partner for Quality Licence Scheme Endorsed courses.</p><p>The Machine Learning with Python course is designed to provide learners with a comprehensive understanding of machine learning and data science using Python programming language. This course covers a wide range of topics, including data analysis, data visualisation, regression, classification, clustering, neural networks, and deep learning. Through this course, learners will develop the skills and knowledge to build and train machine learning models for predictive analysis.</p> <p>The Diploma in Python Programming course is designed to teach learners the fundamentals of Python programming language, including syntax, data types, control structures, functions, and modules. Learners will also learn about the different data structures and algorithms used in Python programming. This course provides a strong foundation for learners who are interested in pursuing a career in software development or data science.</p> <p><strong>Certification</strong></p> <p>In pursuing QLS-endorsed courses, learners attain not only profound understanding but also amplify their professional prowess, enriching their curriculum vitae.</p> <p><strong>The Quality Licence Scheme </strong>stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.</p> <p>Investing in this comprehensive package, learners will not only receive one but TWO HARD COPY QLS ENDORSED CERTIFICATES conveniently <strong>delivered to their doorstep!</strong></p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                             <ul> <li>Students and recent graduates looking to enter the field of programming or machine learning</li><li>Professionals looking to enhance their skills in Python programming and machine learning</li><li>Individuals looking for a career change into programming or machine learning</li><li>Anyone interested in learning about Python programming and machine learning</li><li>Entrepreneurs who want to develop their own machine-learning applications</li></ul> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <ul> <li>Python Developer - £35,000 to £65,000 per year</li><li>Machine Learning Engineer - £45,000 to £85,000 per year</li><li>Data Analyst - £25,000 to £50,000 per year</li><li>Software Engineer - £30,000 to £60,000 per year</li><li>Artificial Intelligence Engineer - £50,000 to £90,000 per year</li></ul> <p>Please note that salary ranges are estimates and may vary depending on qualifications, experience and other factors.</p> 
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Imperial Academy

Level 3 & 5 Endorsed Diploma | QLS Hard Copy Certificate Included | Plus 5 CPD Courses | Lifetime Access

Python (Machine Learning & Programming)

Skill Up

Online Course Creation

Career Development

IT Skills

Flat Discount: 52% OFF! QLS Endorsed| 40 Courses Diploma| 400 CPD Points| Free PDF+Transcript Certificate| Lifetime Access


Level 7 Data Science & Machine Learning (Python, R, SQL & Microsoft Azure) - - QLS Endorsed


<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful.</p>Overview<p>Overview of data science and machine learning at scale<br />Overview of the Hadoop ecosystem<br />Working with HDFS data and Hive tables using Hue<br />Introduction to Cloudera Data Science Workbench<br />Overview of Apache Spark 2<br />Reading and writing data<br />Inspecting data quality<br />Cleansing and transforming data<br />Summarizing and grouping data<br />Combining, splitting, and reshaping data<br />Exploring data<br />Configuring, monitoring, and troubleshooting Spark applications<br />Overview of machine learning in Spark MLlib<br />Extracting, transforming, and selecting features<br />Building and evaluating regression models<br />Building and evaluating classification models<br />Building and evaluating clustering models<br />Cross-validating models and tuning hyperparameters<br />Building machine learning pipelines<br />Deploying machine learning models<br />Spark, Spark SQL, and Spark MLlib<br />PySpark and sparklyr<br />Cloudera Data Science Workbench (CDSW)<br />Hue</p><p>This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful.</p></div><div id="course-pills-2"><h4>Overview of data science and machine learning at scale</h4><h4>Overview of the Hadoop ecosystem</h4><h4>Working with HDFS data and Hive tables using Hue</h4><h4>Introduction to Cloudera Data Science Workbench</h4><h4>Overview of Apache Spark 2</h4><h4>Reading and writing data</h4><h4>Inspecting data quality</h4><h4>Cleansing and transforming data</h4><h4>Summarizing and grouping data</h4><h4>Combining, splitting, and reshaping data</h4><h4>Exploring data</h4><h4>Configuring, monitoring, and troubleshooting Spark applications</h4><h4>Overview of machine learning in Spark MLlib</h4><h4>Extracting, transforming, and selecting features</h4><h4>Building and evauating regression models</h4><h4>Building and evaluating classification models</h4><h4>Building and evaluating clustering models</h4><h4>Cross-validating models and tuning hyperparameters</h4><h4>Building machine learning pipelines</h4><h4>Deploying machine learning models</h4></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Cloudera Data Scientist Training training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Cloudera Data Scientist Training course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Apache Spark

Big Data Analytics

Hadoop

Cloudera Data Scientist Training

<div>
	<h3><b>Course Overview</b></h3>
<p><span>Peek into the world of data science and machine learning with the comprehensive <strong>Data Science &amp; Machine Learning With R in 2021</strong> course. This course will provide you with a detailed understanding of both machine learning and data science. In addition, you will acquire essential skills to pursue a career in this growing industry.</span></p>
<p><span>The <strong>Data Science &amp; Machine Learning With R in 2021</strong> course will teach you the core concept of data science. You will be able to recognize different data types and structures. From the modules, you will receive an introduction to the intermediate R Section. The course will show you the techniques of data manipulation in R. You will know the process of data visualization with R and learn to create reports with R markdown. The Data Science &amp; Machine Learning With R in 2021 course will provide you with an insight into the fundamentals of machine learning. You will understand the principles of data processing, linear regression, logistic regression and more. </span></p>
<p><span>This highly informative <strong>Data Science &amp; Machine Learning With R in 2021</strong> course will equip you with the essential skills of data science. If you desire to become a professional data scientist, this course can be your stepping stone. So, enroll in the course and fast track your career.</span></p>
<h3><b>Learning Outcomes</b></h3>
<ul>
<li><span>Learn the definition of data science </span></li>
<li><span>Understand the basics of machine learning</span></li>
<li><span>Enrich your knowledge of data types and structures</span></li>
<li><span>Know the process of data manipulation in R</span></li>
<li><span>Gain the ability to create reports with R markdown</span></li>
<li><span>Become skilled in building web apps with R shiny</span></li>
</ul>
<h3><b>Who is this course for?</b></h3>
<p><span>Aspiring data scientists or individuals interested in learning data science and machine learning </span></p>
<h3><b>Entry Requirement</b></h3>
<ul>
<li><span>This course is available to all learners, of all academic backgrounds.</span></li>
<li><span>Learners should be aged 16 or over to undertake the qualification.</span></li>
<li><span>Good understanding of English language, numeracy and ICT are required to attend this course.</span></li>
</ul>
<h3><b>Certification</b></h3>
<ul>
<li><span>After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24.</span></li>
<li><span>PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days.</span></li>
</ul>
<h3><b>Why choose us?</b></h3>
<ul>
<li><span>Affordable, engaging &amp; high-quality e-learning study materials;</span></li>
<li><span>Tutorial videos/materials from the industry-leading experts;</span></li>
<li><span>Study in a user-friendly, advanced online learning platform;</span></li>
<li><span>Efficient exam systems for the assessment and instant result;</span></li>
<li><span>The UK &amp; internationally recognized accredited qualification;</span></li>
<li><span>Access to course content on mobile, tablet or desktop from anywhere anytime;</span></li>
<li><span>The benefit of career advancement opportunities;</span></li>
<li><span>24/7 student support via email.</span></li>
</ul>
<h3><b>Career Path</b></h3>
<p><b> </b><span>The <strong>Data Science &amp; Machine Learning With R in 2021</strong></span><b>  </b><span>course is a useful qualification to possess and would be beneficial for any related profession or industry such as:</span></p>
<ul>
<li><span>Data Scientist </span></li>
</ul>
		</div><table width="100%">				<tbody><tr>
					<td colspan="4">Data Science and Machine Learning Course Intro</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Introduction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>What is Data Science</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Overview</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Who is This Course for</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Marketplace</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Job Opportunities</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Types and Structures in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Basics</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Files</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>RStudio</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tidyverse</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Resources</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Types and Structures in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Unit Introduction</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Basic Type</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vector Part One</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors Part Two</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Missing Values</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Coercion</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Naming</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Misc</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Matrics</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>List</td>
					<td> </td>
					<td><span><i></i> 00:32:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Data Frames</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Data Frames</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Frames: Helper Functions</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Frames Tibbles</td>
					<td> </td>
					<td><span><i></i> 00:39:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Intermediate R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Intermediate Introduction</td>
					<td> </td>
					<td><span><i></i> 00:47:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Relational Operations</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Conditional Statements</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Loops</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functions</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Packages</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Factors</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Dates and Times</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functional Programming</td>
					<td> </td>
					<td><span><i></i> 00:37:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Import or Export</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Database1</td>
					<td> </td>
					<td><span><i></i> 00:27:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Manipulation in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Manipulation in R Introduction</td>
					<td> </td>
					<td><span><i></i> 00:36:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tidy Data</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Pipe Operator</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Filter Verb</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Select Verb</td>
					<td> </td>
					<td><span><i></i> 00:46:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Mutate Verb</td>
					<td> </td>
					<td><span><i></i> 00:32:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Arrange Verb</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Summarize Verb</td>
					<td> </td>
					<td><span><i></i> 00:23:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Pivoting</td>
					<td> </td>
					<td><span><i></i> 00:43:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>JSON Parsing</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>String Manipulation</td>
					<td> </td>
					<td><span><i></i> 00:33:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Web Scraping</td>
					<td> </td>
					<td><span><i></i> 00:59:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Visualization in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Visualization in R Section Intro</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Aesthetics Mappings</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Single Variable Plots</td>
					<td> </td>
					<td><span><i></i> 00:37:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Two Variable Plots</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Facets, Layering, and Coordinate Systems</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Styling and Saving</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Creating Reports with R Markdown</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating with R Markdown</td>
					<td> </td>
					<td><span><i></i> 00:29:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Building Webapps with R Shiny</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to R Shiny</td>
					<td> </td>
					<td><span><i></i> 00:26:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>A Basic R Shiny App</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Other Examples with R Shiny</td>
					<td> </td>
					<td><span><i></i> 00:34:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Introduction to Machine Learning</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Part 1</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Part 2</td>
					<td> </td>
					<td><span><i></i> 00:47:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Starting A Career in Data Science</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Starting a Data Science Career Section Overview</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science Resume</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started with Freelancing</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Top Freelance Websites</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Personal Branding</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Importance of Website and Blo</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Networking Do's and Don'ts</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Certificate and Transcript</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Order Your Certificates and Transcripts</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table>

John Academy

Data Science & Machine Learning With R in 2021

<p>Transform the future - one line of code at a time. This comprehensive online bundle equips you with the foundational knowledge and practical skills to explore the exciting world of Artificial Intelligence (AI).</p><p><strong>What you'll gain:</strong></p><ul><li><strong>15 in-depth courses</strong>: Master essential concepts, delve into real-world applications, and explore cutting-edge deep learning projects.</li><li><strong>Structured learning path</strong>: Progress from core AI principles to advanced techniques at your own pace.</li><li><strong>Interactive learning</strong>: Engage with video lectures, quizzes, and practical coding exercises.</li><li><strong>Project-based learning</strong>: Apply your newfound knowledge with hands-on projects in areas like image recognition and social media automation.</li></ul><p>This bundle is designed for anyone curious about AI, from absolute beginners to those seeking to enhance their existing knowledge. The bundle culminates in advanced Deep Learning Projects where you'll build real-world AI applications.</p><p><strong>Course list: </strong></p><p><strong>Course 01: Foundations of Artificial Intelligence:</strong> Explore the core concepts of AI, including machine learning, deep learning, and natural language processing.</p><p><strong>Course 02: Introduction to Statistics:</strong> Grasp the fundamental statistical concepts crucial for understanding and working with AI data.</p><p><strong>Course 03: PIC Microcontroller Simulation:</strong> Gain experience in microcontroller programming, a valuable skill for building physical AI systems.</p><p><strong>Course 04: Probability and Statistics Essential Training:</strong> Strengthen your understanding of probability and statistics, essential tools for analysing AI data.</p><p><strong>Course 05: Statistics &amp; Probability for Data Science &amp; Machine Learning:</strong> Delve deeper into statistical analysis specifically for data science and machine learning applications.</p><p><strong>Course 06: Project on Deep Learning - Artificial Neural Network:</strong> Build your own deep learning model using artificial neural networks.</p><p><strong>Course 07: ChatGPT &amp; AI: 23 Ways to Make Money with AI:</strong> Explore the potential of AI for generating income through various creative and entrepreneurial avenues.</p><p><strong>Course 08: Master in JavaScript Quickly Using ChatGPT Open AI:</strong> Utilise ChatGPT's capabilities to accelerate your mastery of JavaScript programming.</p><p><strong>Course 09: ChatGPT for Marketing and Productivity with AI Tools:</strong> Discover how ChatGPT and other AI tools can enhance your marketing and productivity strategies.</p><p><strong>Course 10: Data Science &amp; Machine Learning with Python:</strong> Learn Python, a popular programming language widely used in data science and machine learning.</p><p><strong>Course 11: Hands-on Machine Learning Project - Covid Mask Detector:</strong> Develop a machine learning model that detects the presence of face masks in images.</p><p><strong>Course 12: Hands-on Machine Learning Project - Auto Image Captioning for Social Media:</strong> Build an AI system that automatically generates captions for your social media images.</p><p><strong>Course 13: Deep Learning Projects - Handwritten Digit Recognition Using Neural Network:</strong> Create a deep learning model that can recognise handwritten digits.</p><p><strong>Course 14: Deep Learning Projects - Convolutional Neural Network:</strong> Explore the power of convolutional neural networks (CNNs) for image recognition tasks.</p><p><strong>Course 15: Deep Learning &amp; Neural Networks Python - Keras:</strong> Learn Keras, a popular Python library for building and training deep learning models.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        40 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Individuals with no prior AI knowledge who want to start their AI journey.</li><li>Professionals seeking to upskill in AI and machine learning.</li><li>Anyone interested in exploring the potential of AI for creative or business applications.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This bundle has minimal entry requirements, requiring only access to a computer or mobile device with internet connectivity, basic computer literacy, proficiency in the English language, and a genuine eagerness to learn. We welcome learners from all backgrounds to join us in exploring new subjects and expanding their knowledge. </p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This bundle opens doors to a range of exciting careers in the booming field of AI, including:</p><ul><li>Machine Learning Engineer</li><li>Data Scientist</li><li>AI Developer</li><li>AI Researcher</li><li>Robotics Engineer</li></ul><p>And many more!</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4> Digital certificate</h4>
        <p>
            Digital certificate -
            £9
        </p>
        <h4> Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div><p>A Hardcopy Certificate (international): £15 + £10 (postal charge) = £25</p></div>

                        </div>

One Education

Artificial Intelligence (Online) Course

<h3><b>Overview</b></h3>
<p><span>In the era where information is abundant and decisions are driven by data, have you ever pondered, 'what is machine learning?' or 'what is data science?' Dive into the realm of '</span><strong><span>Data Science &amp; Machine Learning with R from A-Z</span></strong><span>,' a comprehensive guide to unravel these complexities. This course effortlessly blends the foundational aspects of data science with the intricate depths of machine learning algorithms, all through the versatile medium of R. As the digital economy booms, the demand for machine learning jobs continues to surge. Equip yourself with the proficiency to navigate this dynamic field and transition from being an inquisitive mind to a sought-after professional in the space of data science and machine learning with R.</span></p>
<p><strong><span>Learning Outcomes:</span></strong></p>
<ol>
<li><span>Understand the foundational concepts of data science and machine learning.</span></li>
<li><span>Familiarise oneself with the R environment and its functionalities.</span></li>
<li><span>Master data types, structures, and advanced techniques in R.</span></li>
<li><span>Acquire proficiency in data manipulation and visual representation using R.</span></li>
<li><span>Generate comprehensive reports using R Markdown and design web applications with R Shiny.</span></li>
<li><span>Gain a thorough understanding of machine learning methodologies and their applications.</span></li>
<li><span>Gain insights into initiating a successful career in the data science sector.</span></li>
</ol>
<h3><b>Why buy this Data Science &amp; Machine Learning with R from A-Z course?</b></h3>
<ol>
<li><span>Unlimited access to the course for forever</span></li>
<li><b>Digital Certificate, Transcript, student ID</b><span> all included in the price</span></li>
<li><span>Absolutely no hidden fees</span></li>
<li><span>Directly receive </span><b>CPD </b><span>accredited qualifications after course completion</span></li>
<li><span>Receive one to one assistance on every weekday from professionals</span></li>
<li><span>Immediately receive the PDF certificate after passing</span></li>
<li><span>Receive the original copies of your certificate and transcript on the next working day</span></li>
<li><span>Easily learn the skills and knowledge from the comfort of your home</span></li>
</ol>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials of the </span><b>Data Science &amp; Machine Learning with R from A-Z</b><span> there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60.</span></p>
<h3><b>Who is this Data Science &amp; Machine Learning with R from A-Z course for?</b></h3>
<p><span>This </span><span>course is ideal for</span></p>
<ul>
<li><span>Individuals keen on exploring the intricacies of machine learning and data science.</span></li>
<li><span>Aspiring data analysts and scientists looking to specialise in Machine Learning with R.</span></li>
<li><span>IT professionals aiming to diversify their skill set in the emerging data-driven market.</span></li>
<li><span>Researchers seeking to harness the power of R for data representation and analysis.</span></li>
<li><span>Academics and students aiming to bolster their understanding of modern data practices with R.</span></li>
</ul>
<h3><b>Prerequisites</b></h3>
<p><span>This </span><b>Data Science &amp; Machine Learning with R from A-Z</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Data Science &amp; Machine Learning with R from A-Z</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<h3><b>Career path</b></h3>
<ol>
<li><strong><span>Data Scientist</span></strong><span> - Average salary range: £35,000 - £70,000 Per Annum</span></li>
<li><strong><span>Machine Learning Engineer</span></strong><span> - Average salary range: £50,000 - £80,000 Per Annum</span></li>
<li><strong><span>Data Analyst</span></strong><span> - Average salary range: £28,000 - £55,000 Per Annum</span></li>
<li><strong><span>R Developer</span></strong><span> - Average salary range: £30,000 - £60,000 Per Annum</span></li>
<li><strong><span>R Shiny Web Developer</span></strong><span> - Average salary range: £32,000 - £65,000 Per Annum</span></li>
<li><strong><span>Machine Learning Researcher</span></strong><span> - Average salary range: £40,000 - £75,000 Per Annum</span></li>
</ol><div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4"> Data Science and Machine Learning Course Intro</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Introduction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>What is Data Science</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Overview</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Who is This Course for</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Marketplace</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science and Machine Learning Job Opportunities</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Getting Started with R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Basics</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Files</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>RStudio</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tidyverse</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Resources</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Types and Structures in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Unit Introduction</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Basic Type</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vector Part One</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors Part Two</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Missing Values</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Coercion</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Naming</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Vectors - Misc</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Matrics</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>List</td>
					<td> </td>
					<td><span><i></i> 00:32:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Data Frames</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating Data Frames</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Frames: Helper Functions</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Frames Tibbles</td>
					<td> </td>
					<td><span><i></i> 00:39:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Intermediate R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Intermediate Introduction</td>
					<td> </td>
					<td><span><i></i> 00:47:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Relational Operations</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Conditional Statements</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Loops</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functions</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Packages</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Factors</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Dates and Times</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functional Programming</td>
					<td> </td>
					<td><span><i></i> 00:37:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Import or Export</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Database</td>
					<td> </td>
					<td><span><i></i> 00:27:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Manipulation in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Manipulation in R Introduction</td>
					<td> </td>
					<td><span><i></i> 00:36:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tidy Data</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Pipe Operator</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Filter Verb</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Select Verb</td>
					<td> </td>
					<td><span><i></i> 00:46:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Mutate Verb</td>
					<td> </td>
					<td><span><i></i> 00:32:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Arrange Verb</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Summarize Verb</td>
					<td> </td>
					<td><span><i></i> 00:23:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Pivoting</td>
					<td> </td>
					<td><span><i></i> 00:43:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>JSON Parsing</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>String Manipulation</td>
					<td> </td>
					<td><span><i></i> 00:33:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Web Scraping</td>
					<td> </td>
					<td><span><i></i> 00:59:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Data Visualization in R</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Visualization in R Section Intro</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Aesthetics Mappings</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Single Variable Plots</td>
					<td> </td>
					<td><span><i></i> 00:37:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Two Variable Plots</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Facets, Layering, and Coordinate Systems</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Styling and Saving</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Creating Reports with R Markdown</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Creating with R Markdown</td>
					<td> </td>
					<td><span><i></i> 00:29:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Building Webapps with R Shiny</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to R Shiny</td>
					<td> </td>
					<td><span><i></i> 00:26:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>A Basic R Shiny App</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Other Examples with R Shiny</td>
					<td> </td>
					<td><span><i></i> 00:34:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Introduction to Machine Learning</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Part 1</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning Part 2</td>
					<td> </td>
					<td><span><i></i> 00:47:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Starting A Career in Data Science</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Starting a Data Science Career Section Overview</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Science Resume</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Getting Started with Freelancing</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Top Freelance Websites</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Personal Branding</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Importance of Website and Blo</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Networking Do's and Don'ts</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assignment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment - Data Science &amp; Machine Learning with R</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Studyhub UK

Data Science & Machine Learning with R from A-Z

<section>
						<div>
				<div>
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			<div>
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			<div></div>
		</div>
				</div>
				</div>
				<div>
				<div>
			
				</div>
				<div>
				<div>
					<div><p>Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Data Science &amp; Machine Learning with R Complete Training has been specially designed to help learners gain a good command of Data Science &amp; Machine Learning with R Complete Training, providing them with a solid foundation of knowledge to become a qualified professional.</p><p>Through this Data Science &amp; Machine Learning with R Complete Training, you will gain both practical and theoretical understanding of Data Science &amp; Machine Learning with R Complete Training that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.</p><p>Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.</p></div>
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				<section>
						<div>
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			<div>
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			<h2><b> Learning outcome</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Familiar yourself with the recent development and updates of the relevant industry</span>
									</li>
								<li>
											<span>
													</span>
										<span>Know how to use your theoretical knowledge to adapt in any working environment</span>
									</li>
								<li>
											<span>
													</span>
										<span>Get help from our expert tutors anytime you need</span>
									</li>
								<li>
											<span>
													</span>
										<span>Access to course contents that are designed and prepared by industry professionals</span>
									</li>
								<li>
											<span>
													</span>
										<span>Study at your convenient time and from wherever you want</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Affordable premium-quality E-learning content, you can learn at your own pace.</span>
									</li>
								<li>
											<span>
													</span>
										<span>You will receive a completion certificate upon completing the course.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Internationally recognized Accredited Qualification will boost up your resume.</span>
									</li>
								<li>
											<span>
													</span>
										<span>You will learn the researched and proven approach adopted by successful people to transform their careers.</span>
									</li>
								<li>
											<span>
													</span>
										<span>You will be able to incorporate various techniques successfully and understand your customers better.</span>
									</li>
						</ul>
				</div>
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				<div>
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			<div>
					<div>
				<div>
				<div>
			<h2> <b>Requirements</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>No formal qualifications required, anyone from any academic background can take this course.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Access to a computer or digital device with internet connectivity.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section><div>
                            <div>
                                <h2>Course Curriculum</h2>
                            </div>
                            			<div>
				

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4"><strong>Data Science and Machine Learning Course Intro</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Science and Machine Learning Introduction</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>What is Data Science</td><td><span>🕐 00:10:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Machine Learning Overview</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Who is This Course for</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Science and Machine Learning Marketplace</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Science and Machine Learning Job Opportunities</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Getting Started with R</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Getting Started</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Basics</td><td><span>🕐 00:06:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Files</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>RStudio</td><td><span>🕐 00:07:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Tidyverse</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Resources</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Data Types and Structures in R</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Unit Introduction</td><td><span>🕐 00:30:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Basic Type</td><td><span>🕐 00:09:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vector Part One</td><td><span>🕐 00:20:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vectors Part Two</td><td><span>🕐 00:25:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vectors - Missing Values</td><td><span>🕐 00:16:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vectors - Coercion</td><td><span>🕐 00:14:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vectors - Naming</td><td><span>🕐 00:10:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Vectors - Misc</td><td><span>🕐 00:06:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating Matrics</td><td><span>🕐 00:31:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>List</td><td><span>🕐 00:32:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Introduction to Data Frames</td><td><span>🕐 00:19:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating Data Frames</td><td><span>🕐 00:20:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Frames: Helper Functions</td><td><span>🕐 00:31:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Frames Tibbles</td><td><span>🕐 00:39:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Intermediate R</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Intermediate Introduction</td><td><span>🕐 00:47:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Relational Operations</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Conditional Statements</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Loops</td><td><span>🕐 00:08:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Functions</td><td><span>🕐 00:14:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Packages</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Factors</td><td><span>🕐 00:28:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Dates and Times</td><td><span>🕐 00:30:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Functional Programming</td><td><span>🕐 00:37:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Import or Export</td><td><span>🕐 00:22:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Database</td><td><span>🕐 00:27:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Data Manipulation in R</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Manipulation in R Introduction</td><td><span>🕐 00:36:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Tidy Data</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Pipe Operator</td><td><span>🕐 00:15:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Filter Verb</td><td><span>🕐 00:22:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Select Verb</td><td><span>🕐 00:46:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Mutate Verb</td><td><span>🕐 00:32:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Arrange Verb</td><td><span>🕐 00:10:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>The Summarize Verb</td><td><span>🕐 00:23:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Pivoting</td><td><span>🕐 00:43:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>JSON Parsing</td><td><span>🕐 00:11:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>String Manipulation</td><td><span>🕐 00:33:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Web Scraping</td><td><span>🕐 00:59:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Data Visualization in R</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Visualization in R Section Intro</td><td><span>🕐 00:17:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Getting Started</td><td><span>🕐 00:16:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Aesthetics Mappings</td><td><span>🕐 00:25:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Single Variable Plots</td><td><span>🕐 00:37:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Two Variable Plots</td><td><span>🕐 00:21:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Facets, Layering, and Coordinate Systems</td><td><span>🕐 00:18:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Styling and Saving</td><td><span>🕐 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Creating Reports with R Markdown</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating with R Markdown</td><td><span>🕐 00:29:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Building Webapps with R Shiny</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Introduction to R Shiny</td><td><span>🕐 00:26:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>A Basic R Shiny App</td><td><span>🕐 00:31:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Other Examples with R Shiny</td><td><span>🕐 00:34:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Introduction to Machine Learning</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Machine Learning Part 1</td><td><span>🕐 00:22:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Machine Learning Part 2</td><td><span>🕐 00:47:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Starting A Career in Data Science</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Starting a Data Science Career Section Overview</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Science Resume</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Getting Started with Freelancing</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Top Freelance Websites</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Personal Branding</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Importance of Website and Blo</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Networking Do's and Don'ts</td><td><span>🕐 00:04:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
		                        </div>

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Data Science & Machine Learning with R Complete Training

<div>
						<div>
							<div>
							<section>
						<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
			<h2>Overview
</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p><span>This comprehensive course on <b>Data Science &amp; Machine Learning with R</b> will deepen your understanding on this topic.</span></p><p><span>After successful completion of this course you can acquire the required skills in this sector. This <b>Data Science &amp; Machine Learning with R</b> comes with accredited certification, which will enhance your CV and make you worthy in the job market.</span></p><p><span>So enrol in this course today to fast track your career ladder.</span></p>					</div>
						</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
				<section>
						<div>
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					<div>
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				<section>
						<div>
							<div>
					<div>
			<div>
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			<h2>How will I get my certificate?</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.<br /></p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Who is This course for?</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p><span>There is no experience or previous qualifications required for enrolment on this <b>Data Science &amp; Machine Learning with R</b>. It is available to all students, of all academic backgrounds.</span></p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Requirements</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p><span>Our <b>Data Science &amp; Machine Learning with R</b> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</span></p><p><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Career Path</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p><span>Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business &amp; Management, Admin, Accountancy &amp; Finance, Secretarial &amp; PA, Teaching &amp; Mentoring etc.</span></p>					</div>
						</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
						</div>
						</div>
					</div> <h3>Course Curriculum</h3><div><p>10 sections • 69 lectures • 22:07:00 total length</p></div><div><div><div><div><div><div><div><p>•Data Science and Machine Learning Introduction: 00:03:00</p></div><div><div><p>•What is Data Science: 00:10:00</p></div><div><div><p>•Machine Learning Overview: 00:05:00</p></div><div><div><p>•Who is This Course for: 00:03:00</p></div><div><div><p>•Data Science and Machine Learning Marketplace: 00:05:00</p></div><div><div><p>•Data Science and Machine Learning Job Opportunities: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Getting Started: 00:11:00</p></div><div><div><p>•Basics: 00:06:00</p></div><div><div><p>•Files: 00:11:00</p></div><div><div><p>•RStudio: 00:07:00</p></div><div><div><p>•Tidyverse: 00:05:00</p></div><div><div><p>•Resources: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Unit Introduction: 00:30:00</p></div><div><div><p>•Basic Type: 00:09:00</p></div><div><div><p>•Vector Part One: 00:20:00</p></div><div><div><p>•Vectors Part Two: 00:25:00</p></div><div><div><p>•Vectors - Missing Values: 00:16:00</p></div><div><div><p>•Vectors - Coercion: 00:14:00</p></div><div><div><p>•Vectors - Naming: 00:10:00</p></div><div><div><p>•Vectors - Misc: 00:06:00</p></div><div><div><p>•Creating Matrics: 00:31:00</p></div><div><div><p>•List: 00:32:00</p></div><div><div><p>•Introduction to Data Frames: 00:19:00</p></div><div><div><p>•Creating Data Frames: 00:20:00</p></div><div><div><p>•Data Frames: Helper Functions: 00:31:00</p></div><div><div><p>•Data Frames Tibbles: 00:39:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Intermediate Introduction: 00:47:00</p></div><div><div><p>•Relational Operations: 00:11:00</p></div><div><div><p>•Conditional Statements: 00:11:00</p></div><div><div><p>•Loops: 00:08:00</p></div><div><div><p>•Functions: 00:14:00</p></div><div><div><p>•Packages: 00:11:00</p></div><div><div><p>•Factors: 00:28:00</p></div><div><div><p>•Dates and Times: 00:30:00</p></div><div><div><p>•Functional Programming: 00:37:00</p></div><div><div><p>•Data Import or Export: 00:22:00</p></div><div><div><p>•Database: 00:27:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Manipulation in R Introduction: 00:36:00</p></div><div><div><p>•Tidy Data: 00:11:00</p></div><div><div><p>•The Pipe Operator: 00:15:00</p></div><div><div><p>•The Filter Verb: 00:22:00</p></div><div><div><p>•The Select Verb: 00:46:00</p></div><div><div><p>•The Mutate Verb: 00:32:00</p></div><div><div><p>•The Arrange Verb: 00:10:00</p></div><div><div><p>•The Summarize Verb: 00:23:00</p></div><div><div><p>•Data Pivoting: 00:43:00</p></div><div><div><p>•JSON Parsing: 00:11:00</p></div><div><div><p>•String Manipulation: 00:33:00</p></div><div><div><p>•Web Scraping: 00:59:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Visualization in R Section Intro: 00:17:00</p></div><div><div><p>•Getting Started: 00:16:00</p></div><div><div><p>•Aesthetics Mappings: 00:25:00</p></div><div><div><p>•Single Variable Plots: 00:37:00</p></div><div><div><p>•Two Variable Plots: 00:21:00</p></div><div><div><p>•Facets, Layering, and Coordinate Systems: 00:18:00</p></div><div><div><p>•Styling and Saving: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Creating with R Markdown: 00:29:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction to R Shiny: 00:26:00</p></div><div><div><p>•A Basic R Shiny App: 00:31:00</p></div><div><div><p>•Other Examples with R Shiny: 00:34:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Machine Learning Part 1: 00:22:00</p></div><div><div><p>•Machine Learning Part 2: 00:47:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Starting a Data Science Career Section Overview: 00:03:00</p></div><div><div><p>•Data Science Resume: 00:04:00</p></div><div><div><p>•Getting Started with Freelancing: 00:05:00</p></div><div><div><p>•Top Freelance Websites: 00:05:00</p></div><div><div><p>•Personal Branding: 00:05:00</p></div><div><div><p>•Importance of Website and Blo: 00:04:00</p></div><div><div><p>•Networking Do's and Don'ts: 00:04:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Apex Learning

Online Course Marketing

Trainers Course

Data Science & Machine Learning with R

<p dir="ltr" id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Python for Data Science and Machine Learning Bootcamp course is a comprehensive and practical online course designed to teach you how to use Python for data science and machine learning.&nbsp;</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">The course comprises several sections, each covering different topics in data science and machine learning. It includes all the essential concepts and tools required to analyse and manipulate data, build machine learning models, and make predictions or decisions.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">It also covers dataset summary statistics and visualisation techniques using libraries like matplotlib and seaborn. You&#39;ll learn about model selection, ensemble approaches, and parameter adjustment. Additionally, you will discover how to export and load machine learning models using libraries such as pickle and joblib.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">This course is suitable for anyone interested in learning Python for data science and machine learning. Whether you are an aspiring data scientist or a professional looking to enhance your data analysis skills, this course will help you achieve your goals.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Enrol now to gain instant access to the course materials and start your learning journey. Don&#39;t miss out on the opportunity to acquire one of the most in-demand skills in the market today and become a proficient data scientist and machine learning expert.</span></p><h2 dir="ltr" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Learning Outcome of Python for Data Science and Machine Learning Bootcamp &nbsp; &nbsp;</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Master fundamental concepts of machine learning and its applications.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Gain proficiency in Python programming for data analysis and manipulation.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Learn to load, read, and manipulate CSV data files using Python libraries.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Understand dataset summary statistics and visualization techniques.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Acquire skills in data preparation and preprocessing.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Explore feature selection techniques and their importance.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Evaluate machine learning algorithms using various techniques and metrics.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Spot check classification and regression algorithms for model selection.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Improve model performance using ensemble methods and parameter tuning.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Learn to export, save, load, and finalize machine learning models for real-time predictions.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Key Features of the Course</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">A CPD certificate that is recognised worldwide.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">A great online learning experience.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Interesting and unique online materials and activities.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Expert guidance and support from the field leaders.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Access to the study resources anytime you want.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Friendly and helpful customer service and admin support by email, phone, and chat from Monday through Friday.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Get a year-long access to the course.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Who is this course for</span></h2><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); font-size: 16px; box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0); font-size: 16px;">This Python for Data Science and Machine Learning Bootcamp Course is suitable for -</span></p><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Aspiring data scientists and machine learning enthusiasts</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Professionals seeking to enhance their data analysis skills</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Individuals interested in Python programming for data science</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Anyone looking to break into the field of data science and machine learning</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Students pursuing studies in computer science or related fields</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Requirements</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Basic understanding of programming concepts</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Familiarity with Python programming language is beneficial but not mandatory</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">No prerequisites; suitable for individuals from any academic background.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Accessible course materials from any internet-enabled device.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">CPD Certificate from Course Gate</span></h2><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for &pound;9 and the hard copy for &pound;15. Also, you can order both PDF and hardcopy certificates for &pound;22.</span></p><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Career path</span><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></span></h2><div style="color: rgb(68, 68, 68); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(68, 68, 68);"><div style="color: rgb(68, 68, 68); box-sizing: border-box;" fr-original-style=" color: rgb(68, 68, 68);"><div data-elementor-type="wp-post" data-elementor-id="357815" data-elementor-post-type="course" fr-original-style="" style="box-sizing: border-box;"><section data-id="5767f71a" data-element_type="section" fr-original-style="" style="box-sizing: border-box;"><div data-id="58784751" data-element_type="column" fr-original-style="" style="box-sizing: border-box;"><div data-id="ce105db" data-element_type="widget" data-widget_type="text-editor.default" style="color: var( --e-global-color-text ); box-sizing: border-box;" fr-original-style=" color: var( --e-global-color-text );"><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Scientist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Machine Learning Engineer</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Business Intelligence Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Engineer</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Research Scientist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Quantitative Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Artificial Intelligence Specialist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Consultant</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Statistician</li></ul></div></div></section></div></div></div>

Course Gate

Machine Learning (ML)

Python for Data Science and Machine Learning Bootcamp online course is suitable for anyone interested in learning Python for data science and machine learning. It is especially ideal for aspiring data scientists and professionals seeking to enhance their data analysis skills. 

Python for Data Science and Machine Learning Bootcamp

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to become a data scientist, data engineer, data analyst or consultant</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know the different processes of Data Science, such as data cleaning, processing, wrangling, manipulation</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover the usage of R to clean, analyse, and visualise data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">How to write complex R programs for real-life industry situations</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get data scientist resume tips and land your first job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore the various applications of Machine Learning</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Understand Tidyverse, Operators, Vectors, Lists and their applications</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover data extraction and scraping</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">Data science and Machine Learning are one of the most popular fields in the world today. Data science is crucial to analysing and developing data-based machine learning models. We will cover programming in R in this course for data science and machine learning.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">R programming is a specialised skill that is in high demand today. We&rsquo;ve designed our&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Data Science and Machine Learning with R</b>&nbsp;course to help you learn fundamentals through advanced R programming. This course teaches you how to effectively use R programming for machine learning or data science. You&rsquo;ll learn how to visualise data by plotting in R, as well as how to clean data.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">You will receive training in machine learning and its many real-world applications while working with R Shiny to create web applications and online interactive dashboards. In other words, you will gain practical, step-by-step knowledge of the R programming language that you may use for business or professional purposes.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Data Science and Machine Learning with R&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To explore the world of R Programming Language, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Data Science +ML with R from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Getting Started with R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Types and Structures in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Intermediate R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Manipulation in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Visualization in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Creating Reports with R Markdown</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Building Webapps with R Shiny</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Preprocessing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Simple Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Exploratory Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Real Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Starting a Career in Data Science</strong></li></ul>

LearnDrive UK

The course covers data science and machine learning with R.



Data Science and Machine Learning with R

<strong>This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.</strong>
        <p>Machine learning is the key to development in many areas, such as IT, security, marketing, automation, and even medicine. Without machine learning, it is impossible to build intelligent applications and devices, such as Alexa, Siri, and Google Assistant. This course will help to get familiar with data science and machine learning.

The course starts with an introduction to data science, explaining different terms associated with it. You will also become familiar with machine learning and data science modeling and explore the key differences between model parameters and hyperparameters. Next, you will become familiar with the concepts of machine learning models, such as linear regression, decision trees, random forests, neural networks, and clustering techniques. Towards the end, you will learn how to evaluate machine learning models and learn the best practices to succeed in your data scientist role.

By the end of this course, you will have a solid understanding of data science and machine learning fundamentals.</p>
        <h2>What You Will Learn</h2><p>Become familiar with data science and machine learning terms<br>Distinguish between model parameters and hyperparameters<br>Distinguish between supervised and unsupervised learning<br>Discover how decision trees, bagging, and random forest works<br>Understand the importance of the k-nearest neighbors (KNN) algorithm in machine learning<br>Learn about neural networks and clustering techniques<br>Evaluate the performance of machine learning models</p>
        <h2>Audience</h2><p>This course is designed for students and beginners who want to understand the concepts, statistics, and math behind machine learning algorithms and for those who are curious to solve real-world problems using machine learning and data science. Everything is taught from scratch; hence, there are no prerequisites to get started with this course.</p>
        <h2>Approach</h2><p>With the help of simple explanation, avoiding confusing mathematical notation and jargon, this course covers the fundamentals of data science, machine learning, and data mining from scratch.</p>
        <h2>Key Features</h2><p>Learn the fundamentals of data science, machine learning, and data mining * Learn interesting techniques to evaluate a machine learning model * Discover the best practices to solve real-world problems using machine learning</p>
        
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Welcome and Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome</p><p style='margin: 0'>This video welcomes you to the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Data Science</p><p style='margin: 0'>This video provides an introduction to data science.</p></td></tr></tbody></table><br><h4>2. Preliminary Requirements to Understand Data Science and Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Understanding Data Science Terms</p><p style='margin: 0'>This video provides an overview of the terms used in data science.</p></td></tr><tr><td style='padding: 15px;'><p>2. Understanding Machine Learning</p><p style='margin: 0'>This video presents an overview of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>3. Types of Learning</p><p style='margin: 0'>This video explains the different types of learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Understanding Data Science Modeling</p><p style='margin: 0'>This video focuses on data science modeling.</p></td></tr><tr><td style='padding: 15px;'><p>5. Model parameters versus Hyperparameters</p><p style='margin: 0'>This video explains the difference between model parameters and hyperparameters.</p></td></tr></tbody></table><br><h4>3. Machine Learning Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How Linear Regression Works</p><p style='margin: 0'>This video demonstrates how linear regression works.</p></td></tr><tr><td style='padding: 15px;'><p>2. How Decision Trees Work</p><p style='margin: 0'>This video demonstrates how decision trees work.</p></td></tr><tr><td style='padding: 15px;'><p>3. How Bagging and Random Forest Work</p><p style='margin: 0'>This video demonstrates the working of bagging and random forest.</p></td></tr><tr><td style='padding: 15px;'><p>4. How Support Vector Machine Works</p><p style='margin: 0'>This video demonstrates the working of support vector machines.</p></td></tr><tr><td style='padding: 15px;'><p>5. Neural Networks- Part 1</p><p style='margin: 0'>This video is the first part of a two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>6. Neural Networks- Part 2</p><p style='margin: 0'>This is the second part of the two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>7. How Logistic Regression Works</p><p style='margin: 0'>This video demonstrates the working of logistic regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. How k-nearest neighbour (KNN) Works</p><p style='margin: 0'>This video focuses on KNN and explains how it works.</p></td></tr><tr><td style='padding: 15px;'><p>9. Clustering Techniques</p><p style='margin: 0'>This video explains the different clustering techniques.</p></td></tr></tbody></table><br><h4>4. Model Performance</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Evaluating Models Performance</p><p style='margin: 0'>This video demonstrates how to evaluate the performance of the models.</p></td></tr></tbody></table><br><h4>5. Best Practices for a Data Scientist</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Best Practices for a Data Scientist</p><p style='margin: 0'>This video explains the best practices used by data scientists.</p></td></tr></tbody></table>

Algorithms

This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

Mastering Data Science and Machine Learning Fundamentals

<p>Feeling Stuck in Your Career? The Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle is Your Skill-Building Solution. This exceptional collection of <strong>30 premium courses</strong> is designed to encourage growth and improve your career opportunities. Suited to meet different interests and goals, the  Python for Data Science and Machine Learning bundle provides an engaging learning experience, helping you learn skills across various disciplines.</p><p>With Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle, you'll have a personalised journey that aligns with your career goals and interests. This comprehensive package helps you confidently tackle new challenges, whether entering a new field or enhancing your existing knowledge. The  Python for Data Science and Machine Learning bundle is your gateway to expanding your career options, increasing job demand, and enhancing your skill set.</p><p>By enrolling in this bundle, you'll receive complimentary PDF certificates for all courses, adding value to your resume at no extra cost. Develop key skills and achieve important progress in your career and personal development. Start your journey today and experience the transformative impact of the Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! bundle on your job life and career growth!</p><p><strong>This Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle Comprises the Following CPD Accredited Courses:</strong></p><ul><li><strong>Course 01</strong>: Machine Learning Basics</li><li><strong>Course 02</strong>: Azure Machine Learning</li><li><strong>Course 03</strong>: Python Intermediate Training</li><li><strong>Course 04</strong>: Python Data Science</li><li><strong>Course 05</strong>: 2021 Data Science &amp; Machine Learning with R from A-Z</li><li><strong>Course 06</strong>: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 07</strong>: Learn Python, JavaScript, and Microsoft SQL for Data science</li><li><strong>Course 08</strong>: Spatial Data Visualisation and Machine Learning in Python</li><li><strong>Course 09</strong>: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 10</strong>: Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li><strong>Course 11</strong>: Deep Learning &amp; Neural Networks Python - Keras</li><li><strong>Course 12</strong>: The Complete Python 3 Course: Beginner to Advanced!</li><li><strong>Course 13</strong>: Building Big Data Pipelines with PySpark MongoDB and Bokeh</li><li><strong>Course 14</strong>: Clinical Data Management with SAS Programming</li><li><strong>Course 15</strong>: Certificate in Data Entry and Management</li><li><strong>Course 16</strong>: Quick Data Science Approach from Scratch</li><li><strong>Course 17</strong>: SQL for Data Science, Data Analytics and Data Visualization</li><li><strong>Course 18</strong>: Programming AutoCAD with SQL Server Database Using C#</li><li><strong>Course 19</strong>: Big Data Analytics with PySpark Power BI and MongoDB</li><li><strong>Course 20</strong>: Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</li><li><strong>Course 21</strong>: Develop Big Data Pipelines with R, Sparklyr &amp; Power BI</li><li><strong>Course 22</strong>: Data Center Training Essentials: Power &amp; Electrical</li><li><strong>Course 23</strong>: Business Intelligence and Data Mining</li><li><strong>Course 24</strong>: Data Analysis In Excel</li><li><strong>Course 25</strong>: CV Writing and Job Searching</li><li><strong>Course 26</strong>: Learn to Level Up Your Leadership</li><li><strong>Course 27</strong>: Networking Skills for Personal Success</li><li><strong>Course 28</strong>: Ace Your Presentations: Public Speaking Masterclass</li><li><strong>Course 29</strong>: Learn to Make a Fresh Start in Your Life</li><li><strong>Course 30</strong>: Motivation - Motivating Yourself &amp; Others</li></ul><p><strong>What will make you stand out?</strong></p><p>Upon completion of this online Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle, you will gain the following:</p><ul><li>CPD QS Accredited Proficiency with this  Python for Data Science and Machine Learning  Bundle </li><li>After successfully completing the  Python for Data Science and Machine Learning bundle, you will receive a FREE PDF Certificate from REED as evidence of your newly acquired abilities.</li><li>Lifetime access to the whole collection of learning materials of this  Python for Data Science and Machine Learning       Bundle</li><li>The online test with immediate results </li><li>You can study and complete the  Python for Data Science and Machine Learning bundle at your own pace.</li><li>Study for the  Python for Data Science and Machine Learning bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p>The  Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! bundle is a premier learning resource, with each course module holding respected CPD accreditation, symbolising exceptional quality. The content is packed with knowledge and is regularly updated to ensure it remains relevant. This bundle offers not just education but a constantly improving learning experience designed to enrich both your personal and professional development.</p><p>Advance the future of learning with the   Python for Data Science and Machine Learning bundle, a comprehensive, complete collection of 30 courses. Each course in the  Python for Data Science and Machine Learning bundle has been handpicked by our experts to provide a broad range of learning opportunities. Together, these modules form an important and well-rounded learning experience.</p><p>Our mission is to deliver high-quality, accessible education for everyone. Whether you are starting your career, switching industries, or enhancing your professional skills, the   Python for Data Science and Machine Learning bundle offers the flexibility and convenience to learn at your own pace. Make the   Python for Data Science and Machine Learning package your trusted partner in your lifelong learning journey.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        300 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>The Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! is perfect for:</p><ul><li>Expand your knowledge and skillset for a fulfilling career with the  Python for Data Science and Machine Learning bundle.</li><li>Become a more valuable professional by earning CPD certification and mastering in-demand skills with the  Python for Data Science and Machine Learning bundle.</li><li>Discover your passion or explore new career options with the diverse learning opportunities in the  Python for Data Science and Machine Learning bundle.</li><li>Learn on your schedule, in the comfort of your home - the  Python for Data Science and Machine Learning bundle offers ultimate flexibility for busy individuals.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You are warmly invited to register for this bundle. Please be aware that no formal entry requirements or qualifications are necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Gain a wide range of skills across various fields, improve your problem-solving capabilities, and keep current with industry trends. Perfect for those aiming for career advancement, exploring a new professional direction, or pursuing personal growth. Begin your journey with the  Python for Data Science and Machine Learning bundle.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Certificates</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Compliance Central

Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses!

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to clean, process, wrangle, and manipulate data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know how to create a resume for a data science job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get a grasp of NumPy by learning how to use it for numerical data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover the secret of different Python libraries, such as Pandas, Matplotlib, SciPy and many more</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore machine learning concepts and algorithms</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover developing custom data solutions</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">You can create various applications for science, math, machine learning, and data science with Python. As a high-level, general-purpose programming language, Python is quite English-like and easy to learn. This&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Python for Data Science and Machine Learning</b>&nbsp;course will show you how to use Python for machine learning and data science.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">During the course, you will master every useful Python skill you need for data science and machine learning. If you find it challenging to write complex Python programs, we&rsquo;ll show you how to do so with our step-by-step instructions based on actual work contexts. You&rsquo;ll also learn how to run the regression, classification, and clustering and the specifics of supervised and unsupervised machine learning. Moreover, this course covers how statistics work for data science, hypothesis testing, and much more.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Python for Data Science and Machine Learning&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To become a master of Python, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Python for Data Science &amp; Machine Learning from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science &amp; Machine Learning Concepts</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python For Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Statistics for Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Probability and Hypothesis Testing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">NumPy Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Pandas Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Data Visualization</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Loading &amp; Exploration</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Cleaning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Feature Selecting and Engineering</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear and Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K Nearest Neighbours</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Decision Trees</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Ensemble Learning and Random Forests</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Support Vector Machines</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K-Means</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">PCA</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science Career</strong></li></ul>

This course aims to teach you how to use Python for machine learning and data science.



Python for Data Science and Machine Learning

<strong>In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.</strong>
        <p>The course covers practical issues in statistical computing that include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R programming to mastery.

We understand that theory is important to build a solid foundation, we also understand that theory alone isn't going to get the job done so that's why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you!

R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

By the end of the course, you'll be a professional data scientist with R and confidently apply for jobs and will feel good knowing that you have the skills and knowledge to back it up.

All resources are placed here: https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-</p>
        <h2>What You Will Learn</h2><p>Learn data cleaning, processing, wrangling, and manipulation<br>Learn plotting in R (graphs, charts, plots, histograms, and more)<br>How to create a resume and land your first job as a data scientist<br>Learn machine learning and its various practical applications<br>Learn data and file management in R<br>Use R to clean, analyze, and visualize data</p>
        <h2>Audience</h2><p>This course is designed for beginners who want to learn about data science and machine learning. No prior knowledge of R is required.</p>
        <h2>Approach</h2><p>This course takes you through data analysis in R, data visualization in R, and machine learning with hands-on step-by-step training.</p>
        <h2>Key Features</h2><p>Learn data extraction and web scraping * Learn to build custom data solutions * Learn automating dynamic report generation</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-</p>
        <h2>About the Author</h2><h3>Juan E. Galvan</h3><p>Juan E. Galvan has been an entrepreneur since grade school. His background is in the tech space from digital marketing, e-commerce, web development to programming. He believes in continuous education with the best of a university degree without all the downsides of burdensome costs and inefficient methods. He looks forward to helping people expand their skillsets.</p>
        <h2>Course Outline</h2><h4>1. Data Science and Machine Leaning Course Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Science and Machine Learning Introduction Section Overview</p><p style='margin: 0'>This video explains data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Data Science?</p><p style='margin: 0'>This video explains the concept of data science.</p></td></tr><tr><td style='padding: 15px;'><p>3. Machine Learning Overview</p><p style='margin: 0'>This video gives an overview of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Science + Machine Learning Marketplace</p><p style='margin: 0'>This video explains the various trends and job opportunities in data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>5. Who is this Course For?</p><p style='margin: 0'>This video explains the audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Data Science and Machine Learning Job Opportunities</p><p style='margin: 0'>This video explains the various job opportunities in data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Science Job Roles</p><p style='margin: 0'>This video explains the various job roles for data science.</p></td></tr></tbody></table><br><h4>2. Getting Started with R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Getting Started with R</p><p style='margin: 0'>This video explains the concept of R.</p></td></tr><tr><td style='padding: 15px;'><p>2. R Basics</p><p style='margin: 0'>This video explains the R basics.</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Files</p><p style='margin: 0'>This video explains working with files in R.</p></td></tr><tr><td style='padding: 15px;'><p>4. R Studio</p><p style='margin: 0'>This video explains R studio.</p></td></tr><tr><td style='padding: 15px;'><p>5. Tidyverse Overview</p><p style='margin: 0'>This video provides an overview of tidyverse.</p></td></tr><tr><td style='padding: 15px;'><p>6. Additional Resources</p><p style='margin: 0'>This video shares some additional resources that will help you get along with the course.</p></td></tr></tbody></table><br><h4>3. Data Types and Structures in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Types and Structures in R Section Overview</p><p style='margin: 0'>This video explains data types and structures in R.</p></td></tr><tr><td style='padding: 15px;'><p>2. Basic Types</p><p style='margin: 0'>This video explains basic types in R.</p></td></tr><tr><td style='padding: 15px;'><p>3. Vectors - Part One</p><p style='margin: 0'>This video introduces you to vectors.</p></td></tr><tr><td style='padding: 15px;'><p>4. Vectors - Part Two</p><p style='margin: 0'>This video explains how to interact with R using vectors.</p></td></tr><tr><td style='padding: 15px;'><p>5. Vectors: Missing Values</p><p style='margin: 0'>This video explains vector missing values.</p></td></tr><tr><td style='padding: 15px;'><p>6. Vectors: Coercion</p><p style='margin: 0'>This video explains coercion.</p></td></tr><tr><td style='padding: 15px;'><p>7. Vectors: Naming</p><p style='margin: 0'>This video explains vector naming.</p></td></tr><tr><td style='padding: 15px;'><p>8. Vectors: Miscellaneous</p><p style='margin: 0'>This video explains miscellaneous yet important concepts in vectors.</p></td></tr><tr><td style='padding: 15px;'><p>9. Working with Matrices</p><p style='margin: 0'>This video explains working with matrices.</p></td></tr><tr><td style='padding: 15px;'><p>10. Working with Lists</p><p style='margin: 0'>This video explains working with lists.</p></td></tr><tr><td style='padding: 15px;'><p>11. Introduction to Data Frames</p><p style='margin: 0'>This video explains introduction to data frames.</p></td></tr><tr><td style='padding: 15px;'><p>12. Creating Data Frames</p><p style='margin: 0'>This video explains creating data frames.</p></td></tr><tr><td style='padding: 15px;'><p>13. Data Frames: Helper Functions</p><p style='margin: 0'>This video explains data frames helper functions.</p></td></tr><tr><td style='padding: 15px;'><p>14. Data Frames: Tibbles</p><p style='margin: 0'>This video explains Tibbles in data frames.</p></td></tr></tbody></table><br><h4>4. Intermediate R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Intermedia R Section Introduction</p><p style='margin: 0'>This video introduces you to R section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Relational Operators</p><p style='margin: 0'>This video explains relational operators.</p></td></tr><tr><td style='padding: 15px;'><p>3. Logical Operators</p><p style='margin: 0'>This video explains logical operators,</p></td></tr><tr><td style='padding: 15px;'><p>4. Conditional Statements</p><p style='margin: 0'>This video explains conditional statements.</p></td></tr><tr><td style='padding: 15px;'><p>5. Working with Loops</p><p style='margin: 0'>This video explains working with loops.</p></td></tr><tr><td style='padding: 15px;'><p>6. Working with Functions</p><p style='margin: 0'>This video explains working with functions in R.</p></td></tr><tr><td style='padding: 15px;'><p>7. Working with Packages</p><p style='margin: 0'>This video explains working with packages.</p></td></tr><tr><td style='padding: 15px;'><p>8. Working with Factors</p><p style='margin: 0'>This video explains working with factors in R.</p></td></tr><tr><td style='padding: 15px;'><p>9. Dates and Times</p><p style='margin: 0'>This video explains dates and times in R.</p></td></tr><tr><td style='padding: 15px;'><p>10. Functional Programming</p><p style='margin: 0'>This video explains functional programming.</p></td></tr><tr><td style='padding: 15px;'><p>11. Data Import/Export</p><p style='margin: 0'>This video explains how to import and export data in R.</p></td></tr><tr><td style='padding: 15px;'><p>12. Working with Databases</p><p style='margin: 0'>This video explains working with databases.</p></td></tr></tbody></table><br><h4>5. Data Manipulation in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Manipulation Section Introduction</p><p style='margin: 0'>This video introduces you to data manipulation.</p></td></tr><tr><td style='padding: 15px;'><p>2. Tidy Data</p><p style='margin: 0'>This video explains tidy data.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Pipe Operator</p><p style='margin: 0'>This video explains pipe operator.</p></td></tr><tr><td style='padding: 15px;'><p>4. {dplyr}: The Filter Verb</p><p style='margin: 0'>This video explains the filter verb.</p></td></tr><tr><td style='padding: 15px;'><p>5. {dplyr}: The Select Verb</p><p style='margin: 0'>This video explains the select verb</p></td></tr><tr><td style='padding: 15px;'><p>6. {dplyr}: The Mutate Verb</p><p style='margin: 0'>This video explains the mutate verb.</p></td></tr><tr><td style='padding: 15px;'><p>7. {dplyr}: The Arrange Verb</p><p style='margin: 0'>This video explains the purpose of arrange verb.</p></td></tr><tr><td style='padding: 15px;'><p>8. {dplyr}: The Summarize Verb</p><p style='margin: 0'>This video explains the summarize verb.</p></td></tr><tr><td style='padding: 15px;'><p>9. Data Pivoting: {tidyr}</p><p style='margin: 0'>This video explains data pivoting.</p></td></tr><tr><td style='padding: 15px;'><p>10. String Manipulation: {stringr}</p><p style='margin: 0'>This video explains string manipulation.</p></td></tr><tr><td style='padding: 15px;'><p>11. Web Scraping: {rvest}</p><p style='margin: 0'>This video explains web scraping.</p></td></tr><tr><td style='padding: 15px;'><p>12. JSON Parsing: {jsonlite}</p><p style='margin: 0'>This video explains JSON parsing.</p></td></tr></tbody></table><br><h4>6. Data Visualization in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Visualization in R Section Introduction</p><p style='margin: 0'>This video explains data visualization in R.</p></td></tr><tr><td style='padding: 15px;'><p>2. Getting Started with Data Visualization in R</p><p style='margin: 0'>This video explains getting started with data visualization in R.</p></td></tr><tr><td style='padding: 15px;'><p>3. Aesthetics Mappings</p><p style='margin: 0'>This vide explains aesthetics mappings.</p></td></tr><tr><td style='padding: 15px;'><p>4. Single Variable Plots</p><p style='margin: 0'>This video focuses on single variable plots.</p></td></tr><tr><td style='padding: 15px;'><p>5. Two Variable Plots</p><p style='margin: 0'>This video explains two variable plots.</p></td></tr><tr><td style='padding: 15px;'><p>6. Facets, Layering, and Coordinate Systems</p><p style='margin: 0'>This video explains facets, layering, and coordinate systems.</p></td></tr><tr><td style='padding: 15px;'><p>7. Styling and Saving</p><p style='margin: 0'>This video explains styling and saving.</p></td></tr></tbody></table><br><h4>7. Creating Reports with R Markdown</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to R Markdown</p><p style='margin: 0'>This video is an introduction to R.</p></td></tr></tbody></table><br><h4>8. Building Webapps with R Shiny</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to R Shiny</p><p style='margin: 0'>This video is an introduction to R Shiny</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating a Basic R Shiny App</p><p style='margin: 0'>This video helps you to create a basic R shiny app.</p></td></tr><tr><td style='padding: 15px;'><p>3. Other Examples with R Shiny</p><p style='margin: 0'>This video gives various examples with R shiny.</p></td></tr></tbody></table><br><h4>9. Introduction to Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Machine Learning Part One</p><p style='margin: 0'>This video is an introduction to machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Machine Learning Part Two</p><p style='margin: 0'>This video explains the different approaches in machine learning.</p></td></tr></tbody></table><br><h4>10. Data Preprocessing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Preprocessing Introduction</p><p style='margin: 0'>This video introduces you to data preprocessing.</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Preprocessing</p><p style='margin: 0'>This video explains some of the practical ways of data preprocessing.</p></td></tr></tbody></table><br><h4>11. Linear Regression: A Simple Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression: A Simple Model Introduction</p><p style='margin: 0'>This video explains a simple linear regression model.</p></td></tr><tr><td style='padding: 15px;'><p>2. A Simple Model</p><p style='margin: 0'>This video demonstrates a practical application of machine learning.</p></td></tr></tbody></table><br><h4>12. Exploratory Data Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Exploratory Data Analysis Introduction</p><p style='margin: 0'>This video gives an introduction to exploratory data analysis.</p></td></tr><tr><td style='padding: 15px;'><p>2. Hands-on Exploratory Data Analysis</p><p style='margin: 0'>This video explains hands-on exploratory data analysis.</p></td></tr></tbody></table><br><h4>13. Linear Regression - a Real Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression - Real Model Section Introduction</p><p style='margin: 0'>This video explains linear regression - a real model.</p></td></tr><tr><td style='padding: 15px;'><p>2. Linear Regression in R - Real Model</p><p style='margin: 0'>This video explains linear regression in R.</p></td></tr></tbody></table><br><h4>14. Logistic Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Logistic Regression</p><p style='margin: 0'>This video introduces you to logistic regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression in R</p><p style='margin: 0'>This video explains logistic regression in R.</p></td></tr></tbody></table><br><h4>15. Starting a Career in Data Science</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Starting a Data Science Career Section Overview</p><p style='margin: 0'>This video explains section overview of data science and career.</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating a Data Science Resume</p><p style='margin: 0'>This video explains creating a data science resume.</p></td></tr><tr><td style='padding: 15px;'><p>3. Getting Started with Freelancing</p><p style='margin: 0'>This vide explains how to get started with freelancing.</p></td></tr><tr><td style='padding: 15px;'><p>4. Top Freelance Websites</p><p style='margin: 0'>This video focuses on top freelancing websites.</p></td></tr><tr><td style='padding: 15px;'><p>5. Personal Branding</p><p style='margin: 0'>This video explains personal branding.</p></td></tr><tr><td style='padding: 15px;'><p>6. Networking Do's and Don'ts</p><p style='margin: 0'>This video explains networking do's and don'ts.</p></td></tr><tr><td style='padding: 15px;'><p>7. Setting Up a Website</p><p style='margin: 0'>This video explains how to set up a website.</p></td></tr></tbody></table>

In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

Data Science and Machine Learning with R from A-Z Course [Updated for 2021]

Janets

Data Science and Machine Learning using Python : A Bootcamp

Get involved in a learning adventure, mastering R from foundational basics to advanced techniques. This course is a gateway to the realm of data science. Explore statistical machine learning models and intricacies of deep learning and create interactive Shiny apps. Unleash the power of R and elevate your proficiency in data-driven decision-making.

R Ultimate 2023 - R for Data Science and Machine Learning

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					<div><p>Python has steadily ascended the ranks in the tech world, becoming the go-to language for Data Science and Machine Learning pursuits. Its versatility and ease of use make it an optimal choice for those aspiring to delve into the intricate realms of data analysis and algorithm development. By integrating Python into the curriculum, this course ensures that learners are not only familiar with the language but also proficient in its application to Data Science and Machine Learning tasks.</p><p>Data Science, on the other hand, stands as the backbone of today's analytical advancements. With the ever-growing quantum of data, there's a dire need for professionals who can sift through this data deluge and extract meaningful insights. This course simplifies complex topics, making it easier for learners to grasp the intricate mechanics of data manipulation, processing, and analysis. By emphasizing Python's role in Data Science, the course underscores its significance in modern-day data operations.</p><p>Lastly, Machine Learning represents the pinnacle of automated data analysis. With the power of Python and the methodologies of Data Science, Machine Learning enables systems to learn from data and make decisions. This course elucidates these concepts, teaching learners to design, implement, and test Machine Learning algorithms. The synergy of Python, Data Science, and Machine Learning in this curriculum ensures that, by the end, learners are well-equipped to tackle any data challenge that comes their way.</p></div>
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Master Data Science and Machine Learning with Python to transform data-driven decisions in businesses.

Data Science & Machine Learning with Python

<strong>In the fast-paced world of data science and machine learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for data science and machine learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!</strong>
        <p>The objective of this course is to give you a strong foundation needed to excel in Julia and learn the core of the language as well as the applied side in the shortest amount of time possible.

We won't waste time with the theory of why Julia is fast. We will jump right into the details and start coding. You will quickly realize how easy it is to learn this state-of-the-art and promising language. You will see how you can start using Julia to excel in your current job without moving the whole stack to Julia immediately.

After explaining the basic concepts, we jump to case studies in data science and then machine learning. We apply both traditional machine learning models and then get to deep learning. You will see how Julia can help you create deep learning models from scratch in just a few lines of code and then move on to the state-of-the-art models without spending too much time.

This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own!

By the end of the course, you will have a strong understanding of Julia programming language fundamentals.
The code files are available here: https://github.com/PacktPublishing/Julia-Programming-Language---From-Zero-to-Expert-</p>
        <h2>What You Will Learn</h2><p>Learn coding in Julia programming language<br>Use DataFrames (equivalent to Pandas) in Julia<br>Create ML models from scratch in a way that helps you make modifications easily<br>Learn data wrangling with Julia<br>Use Julia to perform data manipulation, Apache Arrow, grouping, and analysis<br>Classify using decision trees and random forests</p>
        <h2>Audience</h2><p>This course is for all levels of data science and machine learning practitioners aiming to enhance their abilities and skill level in DS and ML. Developers who want to know how to harness the power of big data can also go for this course.<br><br>A basic understanding of programming is a must. Understanding Python, basic data science (reading CSVs and so on), and basic concepts of deep learning (such as classification) is not necessary but would be helpful.</p>
        <h2>Approach</h2><p>This course follows a code-oriented and case-study-based approach. In this course, you will learn by practice as every concept explanation is followed by practical implementation.</p>
        <h2>Key Features</h2><p>Learn the syntax of Julia and its differences from Python * Learn machine learning models, both traditional and deep * Explore data science case studies, including analysis and clustering</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Julia-Programming-Language---From-Zero-to-Expert-</p>
        <h2>About the Author</h2><h3>Dr. Mohammad Nauman</h3><p>Dr. Mohammad Nauman has a PhD in computer science and a PostDoc from the Max Planck Institute for software systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He holds extensive research experience with many state-of-the-art models. His research in Android security has led to some major shifts in the Android permission model.
He loves teaching and the most important reason he teaches online is to make sure that maximum people can learn through his content. Hope you have fun learning with him!</p>
        <h2>Course Outline</h2><h4>1. Intro and Setting up</h4><p></p><br><h4>2. Core Language Basics</h4><p></p><br><h4>3. Arrays and Matrices: Native Language Support</h4><p></p><br><h4>4. Functions and Fun Stuff</h4><p></p><br><h4>5. Getting Started with Data Science</h4><p></p><br><h4>6. Case Studies in Data Science</h4><p></p><br><h4>7. Deep Learning - Flux in Julia</h4><p></p><br><h4>8. Parting Words</h4><p></p>

Programming

In the fast-paced world of data science and machine learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for data science and machine learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!

264 Courses delivered Online

Diploma in Data Science & Machine Learning with R - Level 7 (QLS Endorsed)

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Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python

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Python (Machine Learning & Programming)

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Level 7 Data Science & Machine Learning (Python, R, SQL & Microsoft Azure) - - QLS Endorsed

By Skill Up

Cloudera Data Scientist Training

By Nexus Human

Data Science & Machine Learning With R in 2021

By John Academy

Artificial Intelligence (Online) Course

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Data Science & Machine Learning with R from A-Z

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Data Science & Machine Learning with R Complete Training

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Data Science & Machine Learning with R

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