998 Data Science courses

Course Overview Discover how to become a data scientist, prove hypotheses, and build complex algorithms with this advanced course on Statistics & Probability for Data Science & Machine Learning. This intuitive training will empower you to manipulate records and understand how to break down the most complex processes in this fascinating field. This comprehensive Data Science tutorial delivers the ideal way to learn the methodology and principles needed to excel in this sector. You will be given expert tuition in using all the relevant concepts for analysing information, gain a genuine understanding of these concepts, and attain the skills to excel in appropriate IT commercial industries. Complete this training, and you will have a unique advantage to work in such areas as automobile design, banking service, media forecasting, and much more. This best selling Statistics & Probability for Data Science & Machine Learning has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Statistics & Probability for Data Science & Machine Learning is suitable for anyone who wants to build their professional skill set and improve their expert knowledge. The Statistics & Probability for Data Science & Machine Learning is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The Statistics & Probability for Data Science & Machine Learning is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn. After successfully completing the Statistics & Probability for Data Science & Machine Learning, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Statistics & Probability for Data Science & Machine Learning will help you stand out from the crowd. You can also validate your certification on our website. We know that you are busy and that time is precious, so we have designed the Statistics & Probability for Data Science & Machine Learning to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device.  Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.

About Course Data Science and Data Analytics with Python: A Comprehensive Course for Beginners Unlock the power of data and gain insights that drive informed decisions with this comprehensive course on data science and data analytics with Python. This course is designed for beginners of all skill levels, with no prior programming experience required. You will learn the essential skills to embark on your data-driven journey, including: Data manipulation with NumPy and Pandas Data visualization with Matplotlib and Seaborn Statistical analysis with Python Machine learning and artificial intelligence You will also gain hands-on experience with real-world data projects, allowing you to apply your newfound knowledge to solve real-world problems. By the end of this course, you will be able to: Understand the fundamentals of data science and data analytics Apply Python to manipulate, visualize, and analyze data Use Python to build machine learning and artificial intelligence models Solve real-world data problems This course is the perfect launchpad for your data science journey. Whether you are looking to pivot your career, enhance your skill set, or simply quench your curiosity, this course will give you the foundation you need to succeed. Enroll today and start exploring the fascinating world of data science together! What Will You Learn? Understand the fundamentals of data science and data analytics Apply Python to manipulate, visualize, and analyze data Use Python to build machine learning and artificial intelligence models Solve real-world data problems Course Content Introduction to Python Data Science Introduction to Python Data Science Environment Setup Data Cleaning Packages Working with the Numpy package Working with Pandas Data science package Data Visualization Packages Working with Matplotlib Data Science package (Part - 1) Working with Matplotlib Data Science (Part - 2) A course by Uditha Bandara Microsoft Most Valuable Professional (MVP) RequirementsBeginners level knowledge for working with Data .Programming knowledge not required. Audience Beginners with no prior programming experience Anyone interested in learning data science and data analytics Audience Beginners with no prior programming experience Anyone interested in learning data science and data analytics

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

Dive into Python, Pandas, and NumPy, mastering data manipulation and visualisation for a thriving data science career.

Delve into the world of data analysis with 'R Programming for Data Science,' a course designed to guide learners through the intricacies of R, a premier programming language in the data science domain. The course opens with a broad perspective on data science, illuminating the pivotal role of R in this field. Learners are then introduced to R and RStudio, equipping them with the foundational tools and interfaces essential for R programming. The curriculum progresses with an introduction to the basics of R, ensuring learners grasp the core principles that underpin more complex operations. A highlight of this course is its in-depth exploration of R's versatile data structures, including vectors, matrices, factors, and data frames. Each unit is crafted to provide learners with a comprehensive understanding of these structures, pivotal for effective data handling and manipulation. The course also emphasizes the importance of relational and logical operators in R, key elements for executing data operations. As the course advances, learners will engage with the nuances of conditional statements and loops, essential for writing efficient and dynamic R scripts. Moving into more advanced territories, the course delves into the creation and usage of functions, an integral part of R programming, and the exploration of various R packages that extend the language's capabilities. Learners will also gain expertise in the 'apply' family of functions, crucial for streamlined data processing. Further units cover regular expressions and effective strategies for managing dates and times in data sets. The course concludes with practical applications in data acquisition, cleaning, visualization, and manipulation, ensuring learners are well-prepared to tackle real-world data science challenges using R. Learning Outcomes Develop a foundational understanding of R's role in data science and proficiency in RStudio. Gain fluency in R programming basics, enabling the handling of complex data tasks. Acquire skills in managing various R data structures for efficient data analysis. Master relational and logical operations for advanced data manipulation in R. Learn to create functions and utilize R packages for expanded analytical capabilities. Why choose this R Programming for Data Science course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this R Programming for Data Science course for? Beginners in data science eager to learn R programming. Data analysts and scientists looking to enhance their skills in R. Researchers in various fields requiring advanced data analysis tools. Statisticians seeking to adopt R for more sophisticated data manipulations. Professionals in finance, healthcare, and other sectors needing data-driven insights. Career path Data Scientist (R Expertise): £30,000 - £70,000 Data Analyst (R Programming Skills): £27,000 - £55,000 Bioinformatics Scientist (R Proficiency): £35,000 - £60,000 Quantitative Analyst (R Knowledge): £40,000 - £80,000 Research Analyst (R Usage): £25,000 - £50,000 Business Intelligence Developer (R Familiarity): £32,000 - £65,000 Prerequisites This R Programming for Data Science does not require you to have any prior qualifications or experience. You can just enrol and start learning.This R Programming for Data Science 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. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Unit 01: Data Science Overview Introduction to Data Science 00:01:00 Data Science: Career of the Future 00:04:00 What is Data Science? 00:02:00 Data Science as a Process 00:02:00 Data Science Toolbox 00:03:00 Data Science Process Explained 00:05:00 What's next? 00:02:00 Unit 02: R and RStudio Engine and coding environment 00:03:00 Installing R and RStudio 00:04:00 RStudio: A quick tour 00:04:00 Unit 03: Introduction to Basics Arithmetic with R 00:03:00 Variable assignment 00:04:00 Basic data types in R 00:03:00 Unit 04: Vectors Creating a vector 00:05:00 Naming a vector 00:04:00 Arithmetic calculations on vectors 00:07:00 Vector selection 00:06:00 Selection by comparison 00:04:00 Unit 05: Matrices What's a Matrix? 00:02:00 Analyzing Matrices 00:03:00 Naming a Matrix 00:05:00 Adding columns and rows to a matrix 00:06:00 Selection of matrix elements 00:03:00 Arithmetic with matrices 00:07:00 Additional Materials 00:00:00 Unit 06: Factors What's a Factor? 00:02:00 Categorical Variables and Factor Levels 00:04:00 Summarizing a Factor 00:01:00 Ordered Factors 00:05:00 Unit 07: Data Frames What's a Data Frame? 00:03:00 Creating Data Frames 00:20:00 Selection of Data Frame elements 00:03:00 Conditional selection 00:03:00 Sorting a Data Frame 00:03:00 Additional Materials 00:00:00 Unit 08: Lists Why would you need lists? 00:01:00 Creating a List 00:06:00 Selecting elements from a list 00:03:00 Adding more data to the list 00:02:00 Additional Materials 00:00:00 Unit 09: Relational Operators Equality 00:03:00 Greater and Less Than 00:03:00 Compare Vectors 00:03:00 Compare Matrices 00:02:00 Additional Materials 00:00:00 Unit 10: Logical Operators AND, OR, NOT Operators 00:04:00 Logical operators with vectors and matrices 00:04:00 Reverse the result: (!) 00:01:00 Relational and Logical Operators together 00:06:00 Additional Materials 00:00:00 Unit 11: Conditional Statements The IF statement 00:04:00 IFELSE 00:03:00 The ELSEIF statement 00:05:00 Full Exercise 00:03:00 Additional Materials 00:00:00 Unit 12: Loops Write a While loop 00:04:00 Looping with more conditions 00:04:00 Break: stop the While Loop 00:04:00 What's a For loop? 00:02:00 Loop over a vector 00:02:00 Loop over a list 00:03:00 Loop over a matrix 00:04:00 For loop with conditionals 00:01:00 Using Next and Break with For loop 00:03:00 Additional Materials 00:00:00 Unit 13: Functions What is a Function? 00:02:00 Arguments matching 00:03:00 Required and Optional Arguments 00:03:00 Nested functions 00:02:00 Writing own functions 00:03:00 Functions with no arguments 00:02:00 Defining default arguments in functions 00:04:00 Function scoping 00:02:00 Control flow in functions 00:03:00 Additional Materials 00:00:00 Unit 14: R Packages Installing R Packages 00:01:00 Loading R Packages 00:04:00 Different ways to load a package 00:02:00 Additional Materials 00:00:00 Unit 15: The Apply Family - lapply What is lapply and when is used? 00:04:00 Use lapply with user-defined functions 00:03:00 lapply and anonymous functions 00:01:00 Use lapply with additional arguments 00:04:00 Additional Materials 00:00:00 Unit 16: The apply Family - sapply & vapply What is sapply? 00:02:00 How to use sapply 00:02:00 sapply with your own function 00:02:00 sapply with a function returning a vector 00:02:00 When can't sapply simplify? 00:02:00 What is vapply and why is it used? 00:04:00 Additional Materials 00:00:00 Unit 17: Useful Functions Mathematical functions 00:05:00 Data Utilities 00:08:00 Additional Materials 00:00:00 Unit 18: Regular Expressions grepl & grep 00:04:00 Metacharacters 00:05:00 sub & gsub 00:02:00 More metacharacters 00:04:00 Additional Materials 00:00:00 Unit 19: Dates and Times Today and Now 00:02:00 Create and format dates 00:06:00 Create and format times 00:03:00 Calculations with Dates 00:03:00 Calculations with Times 00:07:00 Additional Materials 00:00:00 Unit 20: Getting and Cleaning Data Get and set current directory 00:04:00 Get data from the web 00:04:00 Loading flat files 00:03:00 Loading Excel files 00:05:00 Additional Materials 00:00:00 Unit 21: Plotting Data in R Base plotting system 00:03:00 Base plots: Histograms 00:03:00 Base plots: Scatterplots 00:05:00 Base plots: Regression Line 00:03:00 Base plots: Boxplot 00:03:00 Unit 22: Data Manipulation with dplyr Introduction to dplyr package 00:04:00 Using the pipe operator (%>%) 00:02:00 Columns component: select() 00:05:00 Columns component: rename() and rename_with() 00:02:00 Columns component: mutate() 00:02:00 Columns component: relocate() 00:02:00 Rows component: filter() 00:01:00 Rows component: slice() 00:04:00 Rows component: arrange() 00:01:00 Rows component: rowwise() 00:02:00 Grouping of rows: summarise() 00:03:00 Grouping of rows: across() 00:02:00 COVID-19 Analysis Task 00:08:00 Additional Materials 00:00:00 Assignment Assignment - R Programming for Data Science 00:00:00

In this course, you will learn how to perform data cleaning and data preparation with KNIME and without coding. You should be familiar with KNIME as no basics are covered in this course. Basic knowledge of machine learning is certainly helpful for the later lectures in this course.

In today's data-driven world, 82% of businesses demand Data Science professionals - and they're willing to pay for it! With average global salaries of Data Scientists and Machine Learning experts surpassing $120,000 annually, it's clear: the future belongs to those who understand data. Our Advanced Diploma in Data Science & Machine Learning - Level 7 is meticulously designed to offer profound knowledge and skills . This Data Science & Machine Learning bundle encompasses a comprehensive selection, from the nuances of Statistics & Probability to the intriguing applications of Machine Learning in Flutter. Also, this comprehensive Data Science & Machine Learning bundle ensures you're well-equipped with the essential knowledge required in today's competitive global market. By opting for this Data Science & Machine Learning advanced diploma, you're not just learning - you're investing in a future filled with opportunities, lucrative incomes, and immense professional demand globally. Data Science & Machine Learning Course Learning Outcomes Grasp the fundamentals of Statistics & Probability. Acquire proficiency in Python for Data Science. Understand R programming in data analytics context. Dive deep into MySQL and its applications in Data Science. Master SQL techniques for Data Visualization and Analytics. Lay a strong foundation in basic machine learning concepts. Navigate through Azure Machine Learning and its utilities. This Data Science & Machine Learning bundle course, however, is designed to be your compass, guiding you through the intricacies of the most pivotal topics. With the Advanced Diploma, you delve deep into the mathematical bedrock of data science through Statistics & Probability. Python and R, the stalwarts of data programming, are unravelled, offering insights into their unique capabilities. SQL's unmatched prowess in data visualisation and analytics is highlighted, while Azure provides a window into the world of cloud-based machine learning. Lastly, understand the synergy of machine learning and mobile applications through Flutter. The benefits? A holistic understanding, positioning you ahead in the academic sphere and offering a competitive edge should you venture into research or further studies. Courses in this Data Science & Machine Learning bundle: Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7 Data Science & Machine Learning with Python R Programming for Data Science Learn MySQL from Scratch for Data Science and Analytics SQL for Data Science, Data Analytics and Data Visualization Machine Learning Basics "Azure Machine Learning" Machine Learning use in Flutter, The Complete Guide Certificate of Achievement Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the Data Science & Machine Learning course as proof of their achievement. You can order the endorsed certificate for only £129 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this Data Science & Machine Learning course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the Data Science & Machine Learning course has been reviewed and approved by the QLS and meets the highest quality standards. CPD 180 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Data Science & Machine Learning bundle is ideal for: Aspiring Data Scientists. Statisticians looking to expand into Machine Learning. Developers keen on integrating Machine Learning in apps. Research scholars in data-driven fields. Requirements To enrol in this Data Science & Machine Learning course, all you need is a basic understanding of the English Language and an internet connection. Career path Data Scientist Machine Learning Engineer Data Analyst SQL Database Administrator Research Scientist (Data Specialization) Flutter Developer with ML Integration Certificates 8 CPD Accredited PDF Certificate Digital certificate - Included CPD Accredited Hard Copy Certificate Hard copy certificate - Included Delivery Charge: Inside the UK: £3.99 Outside the UK: £9.99 QLS Endorsed Hard Copy Certificate Hard copy certificate - Included Delivery Charge: Inside the UK: Free Outside the UK: £9.99 Other CPD Accredited Hard Copy Certificate Hard copy certificate - £9.99 Free Courses Hard Copy Certificates Are £9.99 Each. Delivery Charge: Inside the UK: Free Outside the UK: £9.99

Get ready for an exceptional online learning experience with the Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security bundle! This carefully curated collection of 20 premium courses is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations. The Python & Data Science package is dynamic, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you're looking to break into a new field or deepen your existing knowledge, the Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Securitypackage has something for everyone. As part of the Python & Data Science package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Python & Data Science bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth! This Bundle Comprises the Following Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security CPD-accredited courses: Course 01: Python Programming: Beginner To Expert Course 02: Data Science & Machine Learning with Python Course 03: Coding with Python 3 Course 04: Introduction to Coding With HTML, CSS, & Javascript Course 05: Python for Spatial Analysis in ArcGIS Course 06: Python Programming Bible | Networking, GUI, Email, XML, CGI Course 07: Business Intelligence and Data Mining Course 08: SQL for Data Science, Data Analytics and Data Visualization Course 09: Python Data Science with Numpy, Pandas and Matplotlib Course 10: Cloud Computing / CompTIA Cloud+ (CV0-002) Course 11: Cyber Security Awareness Training Course 12: Learn Ethical Hacking From A-Z: Beginner To Expert Course 13: Easy to Advanced Data Structures Course 14: R Programming for Data Science Course 15: GDPR UK Training Course 16: Career Development Plan Fundamentals Course 17: CV Writing and Job Searching Course 18: Learn to Level Up Your Leadership Course 19: Networking Skills for Personal Success Course 20: Ace Your Presentations: Public Speaking Masterclass What will make you stand out? Upon completion of this online Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security bundle, you will gain the following: CPD QS Accredited Proficiency with this Python & Data Science bundle After successfully completing the Python & Data Science bundle, you will receive a FREE PDF Certificate from REED as evidence of your newly acquired abilities. Lifetime access to the whole collection of learning materials of this Python & Data Science bundle The online test with immediate results You can study and complete the Python & Data Science bundle at your own pace. Study for the Python & Data Science bundle using any internet-connected device, such as a computer, tablet, or mobile device. Each course in this Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This Python & Data Science bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development. Embrace the future of learning with the Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security , a rich anthology of 30 diverse courses. Our experts handpick each course in the Python & Data Science bundle to ensure a wide spectrum of learning opportunities. This Python & Data Science bundle will take you on a unique and enriching educational journey. The Python & Data Science bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Python & Data Science bundle offers you the flexibility and convenience to learn at your own pace. Make the Python & Data Science package your trusted companion in your lifelong learning journey. CPD 200 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security bundle is perfect for: Beginners interested in technology: Individuals with little to no background in tech who are looking to start their careers in various IT domains. Aspiring Data Scientists and Analysts: Those aiming to build foundational and advanced skills in data science and machine learning. Future Software Developers: Individuals interested in learning Python and SQL for software development or database management. IT Security Enthusiasts: People who are keen on entering the field of cyber security and want to understand how to protect data in a digital environment. Cloud Computing Aspirants: Those looking to gain skills in cloud technologies and understand how to manage and deploy applications on the cloud. Requirements You are warmly invited to register for this Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security 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. Career path Upon Python, Data Science, Machine Learning, SQL, Cloud computing & Cyber Security course completion, you can expect to: Python Developer Data Analyst Machine Learning Engineer Database Administrator Cloud Solutions Architect Cyber Security Specialist Data Science Consultant Systems Analyst Network Security Engineer Research Scientist (AI/ML) Certificates 20 CPD Quality Standard Pdf Certificates Digital certificate - Included

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Data Is The Language Of The Powerholders | Designed By Industry Specialists | Level 7 QLS Endorsed Career Objective Driven Data Science Courses | 10 QLS Endorsed Hard Copy Certificates Included | Lifetime Access | Installment Payment | Tutor Support