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This is a complete crash course about KNIME for beginners. Here, we will learn how to do data cleaning and data preparation without any code, using KNIME. We will also cover data visualization using Tableau and Power BI Desktop. Then we will understand the predictive analytics capabilities of KNIME and finally, cover machine learning in KNIME.

Overview 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 'Data Science & Machine Learning with R from A-Z,' 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. Learning Outcomes: Understand the foundational concepts of data science and machine learning. Familiarise oneself with the R environment and its functionalities. Master data types, structures, and advanced techniques in R. Acquire proficiency in data manipulation and visual representation using R. Generate comprehensive reports using R Markdown and design web applications with R Shiny. Gain a thorough understanding of machine learning methodologies and their applications. Gain insights into initiating a successful career in the data science sector. Why buy this Data Science & Machine Learning with R from A-Z course? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Data Science & Machine Learning with R from A-Z 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. Who is this Data Science & Machine Learning with R from A-Z course for? This course is ideal for Individuals keen on exploring the intricacies of machine learning and data science. Aspiring data analysts and scientists looking to specialise in Machine Learning with R. IT professionals aiming to diversify their skill set in the emerging data-driven market. Researchers seeking to harness the power of R for data representation and analysis. Academics and students aiming to bolster their understanding of modern data practices with R. Prerequisites This Data Science & Machine Learning with R from A-Z does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Data Science & Machine Learning with R from A-Z 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. Career path Data Scientist - Average salary range: £35,000 - £70,000 Per Annum Machine Learning Engineer - Average salary range: £50,000 - £80,000 Per Annum Data Analyst - Average salary range: £28,000 - £55,000 Per Annum R Developer - Average salary range: £30,000 - £60,000 Per Annum R Shiny Web Developer - Average salary range: £32,000 - £65,000 Per Annum Machine Learning Researcher - Average salary range: £40,000 - £75,000 Per Annum Course Curriculum Data Science and Machine Learning Course Intro Data Science and Machine Learning Introduction 00:03:00 What is Data Science 00:10:00 Machine Learning Overview 00:05:00 Who is This Course for 00:03:00 Data Science and Machine Learning Marketplace 00:05:00 Data Science and Machine Learning Job Opportunities 00:03:00 Getting Started with R Getting Started 00:11:00 Basics 00:06:00 Files 00:11:00 RStudio 00:07:00 Tidyverse 00:05:00 Resources 00:04:00 Data Types and Structures in R Unit Introduction 00:30:00 Basic Type 00:09:00 Vector Part One 00:20:00 Vectors Part Two 00:25:00 Vectors - Missing Values 00:16:00 Vectors - Coercion 00:14:00 Vectors - Naming 00:10:00 Vectors - Misc 00:06:00 Creating Matrics 00:31:00 List 00:32:00 Introduction to Data Frames 00:19:00 Creating Data Frames 00:20:00 Data Frames: Helper Functions 00:31:00 Data Frames Tibbles 00:39:00 Intermediate R Intermediate Introduction 00:47:00 Relational Operations 00:11:00 Conditional Statements 00:11:00 Loops 00:08:00 Functions 00:14:00 Packages 00:11:00 Factors 00:28:00 Dates and Times 00:30:00 Functional Programming 00:37:00 Data Import or Export 00:22:00 Database 00:27:00 Data Manipulation in R Data Manipulation in R Introduction 00:36:00 Tidy Data 00:11:00 The Pipe Operator 00:15:00 The Filter Verb 00:22:00 The Select Verb 00:46:00 The Mutate Verb 00:32:00 The Arrange Verb 00:10:00 The Summarize Verb 00:23:00 Data Pivoting 00:43:00 JSON Parsing 00:11:00 String Manipulation 00:33:00 Web Scraping 00:59:00 Data Visualization in R Data Visualization in R Section Intro 00:17:00 Getting Started 00:16:00 Aesthetics Mappings 00:25:00 Single Variable Plots 00:37:00 Two Variable Plots 00:21:00 Facets, Layering, and Coordinate Systems 00:18:00 Styling and Saving 00:12:00 Creating Reports with R Markdown Creating with R Markdown 00:29:00 Building Webapps with R Shiny Introduction to R Shiny 00:26:00 A Basic R Shiny App 00:31:00 Other Examples with R Shiny 00:34:00 Introduction to Machine Learning Machine Learning Part 1 00:22:00 Machine Learning Part 2 00:47:00 Starting A Career in Data Science Starting a Data Science Career Section Overview 00:03:00 Data Science Resume 00:04:00 Getting Started with Freelancing 00:05:00 Top Freelance Websites 00:05:00 Personal Branding 00:05:00 Importance of Website and Blo 00:04:00 Networking Do's and Don'ts 00:04:00 Assignment Assignment - Data Science & Machine Learning with R 00:00:00

Overview Uplift Your Career & Skill Up to Your Dream Job - Learning Simplified From Home! Kickstart your career & boost your employability by helping you discover your skills, talents, and interests with our special 2021 Data Science & Machine Learning with R from A-Z Course. You'll create a pathway to your ideal job as this course is designed to uplift your career in the relevant industry. It provides the professional training that employers are looking for in today's workplaces. The 2021 Data Science & Machine Learning with R from A-Z  Course is one of the most prestigious training offered at Skillwise and is highly valued by employers for good reason. This 2021 Data Science & Machine Learning with R from A-Z Course Course has been designed by industry experts to provide our learners with the best learning experience possible to increase their understanding of their chosen field. This 2021 Data Science & Machine Learning with R from A-Z Course, like every one of Skillwise's courses, is meticulously developed and well-researched. Every one of the topics is divided into elementary modules, allowing our students to grasp each lesson quickly. At Skillwise, we don't just offer courses; we also provide a valuable teaching process. When you buy a course from Skillwise, you get unlimited Lifetime access with 24/7 dedicated tutor support. Why buy this 2021 Data Science & Machine Learning with R from A-Z Course ? Lifetime access to the course forever Digital Certificate, Transcript, and student ID are all included in the price Absolutely no hidden fees Directly receive CPD Quality Standard-accredited qualifications after course completion Receive one-to-one assistance every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the 2021 Data Science & Machine Learning with R from A-Z Course 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 free. Original Hard Copy certificates need to be ordered at an additional cost of £8. Who is this course for? This 2021 Data Science & Machine Learning with R from A-Z  course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already work in relevant fields and want to polish their knowledge and skills. Prerequisites This 2021 Data Science & Machine Learning with R from A-Z Course does not require you to have any prior qualifications or experience. You can just enrol and start learning. This 2021 Data Science & Machine Learning with R from A-Z Course was made by professionals and it is compatible with all PCs, Macs, 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. Career path As this course comes with multiple courses included as a bonus, you will be able to pursue multiple occupations. This 2021 Data Science & Machine Learning with R from A-Z Course is a great way for you to gain multiple skills from the comfort of your home.

Get ready for an exceptional online learning experience with the Python, Data Science, Machine Learning, Data Mining & 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. This Python, Data Science, Machine Learning, Data Mining & Cyber Security is a dynamic package, 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 package has something for everyone. As part of the Python, Data Science, Machine Learning, Data Mining & Cyber Security package, you will receive complimentary PDF certificates for all courses in Python & Data Science bundle at no extra cost. 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ThisBundle Comprises the Following CPD Accredited Courses: Python Programming: Beginner To Expert Data Science & Machine Learning with Python Coding with Python 3 Introduction to Coding With HTML, CSS, & Javascript Python for Spatial Analysis in ArcGIS Python Programming Bible | Networking, GUI, Email, XML, CGI Business Intelligence and Data Mining SQL for Data Science, Data Analytics and Data Visualization Python Data Science with Numpy, Pandas and Matplotlib Cloud Computing / CompTIA Cloud+ (CV0-002) Cyber Security Awareness Training Learn Ethical Hacking From A-Z: Beginner To Expert Easy to Advanced Data Structures R Programming for Data Science GDPR UK Training Career Development Plan Fundamentals CV Writing and Job Searching Learn to Level Up Your Leadership Networking Skills for Personal Success Ace Your Presentations: Public Speaking Masterclass Learning Outcome: By completing the course, you will: Gain comprehensive insights into multiple fields. 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Embrace the future of learning with the Python, Data Science, Machine Learning, Data Mining & Cyber Security , a rich anthology of 15 diverse courses. Each course in the Python & Data Science bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. This Python, Data Science, Machine Learning, Data Mining & Cyber Security bundle will take you on a unique and enriching educational journey. The 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, Machine Learning, Data Mining & Cyber Security 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? 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Certificates Certificate Of Completion Hard copy certificate - Included You will get a complimentary Hard Copy Certificate. Certificate Of Completion Digital certificate - Included

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Register on the Machine Learning in Flutter today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion. The Machine Learning in Flutter course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The Machine Learning in Flutter Course Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the Machine Learning in Flutter course, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content Unit 01: Introduction Module 01: Course Curriculum 00:02:00 Unit 02: Image Picker and Camera Libraries Module 01: Image Picker Library for Flutter App Development 00:13:00 Module 02: Flutter Image Picker Application Testing 00:01:00 Module 03: Camera Package Setup for Flutter 00:04:00 Module 04: Flutter Camera Package Code 00:08:00 Unit 03: Firebase ML Kit Module 01: Firebase ML kit section Introduction 00:01:00 Module 02: Firebase ML Kit introduction 00:02:00 Unit 04: Image Labeling using ML Kit Module 01: Flutter Image Labeling Section Introduction 00:02:00 Module 02: Importing Starter code for image labeling 00:03:00 Module 03: Image labeling starter code explanation 00:06:00 Module 04: Creating firebase project for image labeling 00:06:00 Module 05: Adding Firebase ML Vision library in Flutter Application 00:10:00 Module 06: Testing Firebase Image labeling application 00:01:00 Module 07: Importing Image Labeling live feed application starter code 00:03:00 Module 08: Flutter Camera Package Code 00:06:00 Module 09: Flutter Image Labeling live feed application code 00:08:00 Module 10: Flutter Image labeling live feed application testing 00:01:00 Unit 05: Section Barcode Scanning Module 01: Flutter Barcode Scanning Section Introduction 00:02:00 Module 02: Importing Starter code for Flutter Barcode Scanning 00:03:00 Module 03: Flutter Barcode Scanning code 00:11:00 Module 04: Flutter Barcode Scanning Application Testing 00:01:00 Module 05: Flutter Barcode Scanning Live Feed Application code 00:08:00 Module 06: Flutter Barcode Scanning Live feed Application Testing 00:01:00 Unit 06: Section Text Recognition Module 01: Flutter Text Recognition Section Introduction 00:01:00 Module 02: Importing Starter code for Flutter Text Recognition 00:03:00 Module 03: Writing Flutter Text Recognition Code 00:09:00 Module 04: Testing Flutter Text Recognition Application 00:01:00 Unit 07: Section Face Detection Module 01: Flutter Face Detection Section Introduction 00:02:00 Module 02: Flutter Face Detection Application Flow 00:01:00 Module 03: Flutter Face Detection code 00:06:00 Module 04: Flutter drawing rectangles around detected faces 00:05:00 Unit 08: Pretrained Tensorflow lite models Module 01: Pretrained Tensorflow lite models Section Introduction 00:02:00 Unit 09: Section Image Classification Module 01: Flutter Image classification Section introduction 00:02:00 Module 02: Importing Starter code for Flutter Image classification application 00:03:00 Module 03: Starter code explanation for Flutter Image classification 00:06:00 Module 04: Writing flutter image classification code 00:13:00 Module 05: Testing flutter image classification application 00:02:00 Module 06: Importing Flutter live feed Image classification application starter code 00:03:00 Module 07: Starter code explanation of Flutter Live feed Image classification application 00:05:00 Module 08: Writing Flutter Image classification code 00:11:00 Module 09: Testing live feed image classification flutter application 00:01:00 Unit 10: Section object detection Module 01: Flutter Object detection section introduction 00:02:00 Module 02: Importing Application code object detection flutter 00:05:00 Module 03: Flutter Object detection code 00:13:00 Module 04: Flutter Drawing Rectangles around detected objects 00:04:00 Module 05: Importing the code for live feed object detection flutter application 00:02:00 Module 06: Testing object detection live feed flutter application 00:01:00 Module 07: Flutter Live feed object detection application code 00:10:00 Unit 11: Section human pose estimation Module 01: Flutter Pose estimation section introduction 00:02:00 Module 02: Importing Flutter Pose estimation Application code 00:04:00 Module 03: Flutter Pose estimation code 00:10:00 Module 04: Importing pose estimation live feed flutter application code 00:02:00 Module 05: Flutter Live feed pose estimation application demo 00:09:00 Module 06: Using PoseNet model for Flutter Live feed pose estimation application 00:08:00 Unit 12: Image segmentation section Module 01: Flutter Image Segmentation Section Introduction 00:02:00 Module 02: Importing Flutter Image Segmentation Application code 00:03:00 Module 03: Flutter using DeepLab model for image segmentation 00:09:00 Unit 13: Section Training Image Classification Models Module 01: Section Introduction 00:02:00 Module 02: Machine Learning and Image classification 00:02:00 Unit 14: Dog Breed Classification Module 01: Flutter getting the dataset for model training 00:05:00 Module 02: Flutter Training the model 00:06:00 Module 03: Flutter Dog Breed Classification Application 00:18:00 Module 04: Flutter Live feed dog breed classification application 00:03:00 Module 05: Testing live feed dog breed classification application 00:01:00 Unit 15: Fruits Recognition using Transfer Learning Module 01: Transfer learning introduction 00:02:00 Module 02: Flutter getting the dataset for model training 00:05:00 Module 03: Flutter Training fruit recognition model 00:09:00 Module 04: Flutter Testing Live feed fruits recognition application 00:01:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

You will learn Python-based deep learning and machine learning techniques through this course. With numerous real-world case studies, we will go over all the mathematics needed to master deep learning algorithms. We will study Backpropagation, Feed Forward Network, Artificial Neural Networks, CNN, RNN, Transfer Learning, and more.

If you are working on data science projects and want to create powerful visualization and insights as an outcome of your projects or are working on machine learning projects and want to find patterns and insights from your data on your way to building models, then this course is for you. This course exclusively focuses on explaining how to build fantastic visualizations using Python. It covers more than 20 types of visualizations using the most popular Python visualization libraries, such as Matplotlib, Seaborn, and Bokeh along with data analytics that leads to building these visualizations so that the learners understand the flow of analysis to insights.

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

In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.