1. Course Introduction FREE CHAPTER

• Course Overview
• R and RStudio (Overview and Installation)
• How to Get the Code?
• RStudio Introduction / Project Setup
• File Formats
• Rmarkdown Lab

2. Data Types and Structures

• Basic Data Types 101
• Basic Data Types Lab
• Matrices and Arrays Lab
• Lists
• Factors
• Dataframes
• Strings Lab
• Datetime

3. R Programming

• Operators
• Loops 101
• Loops Lab
• Functions 101
• Functions Lab (Introduction)
• Functions Lab (Coding)

4. Data Import and Export

• Data Import Lab
• Data Export Lab
• Web Scraping Introduction
• Web Scraping Lab

5. Basic Data Manipulation

• Piping 101
• Filtering 101
• Filtering Lab
• Data Aggregation 101
• Data Aggregation Lab
• Data Reshaping 101
• Data Reshaping Lab
• Set Operations 101
• Set Operations Lab
• Joining Datasets 101
• Joining Datasets Lab

6. Data Visualization

• Visualization Overview
• ggplot 101
• ggplot Lab
• plotly Lab (Introduction)
• plotly Lab
• leaflet Lab (Introduction)
• leaflet Lab
• dygraphs Lab (Introduction)
• dygraphs Lab

7. Advanced Data Manipulation

• Outlier Detection 101
• Outlier Detection Lab (Introduction)
• Outlier Detection Solution
• Missing Data Handling 101
• Missing Data Handling Lab (Introduction)
• Missing Data Handling Lab (1/1)
• Regular Expressions 101
• Regular Expressions Lab

8. Machine Learning: Introduction

• AI 101
• Machine Learning 101
• Models

9. Machine Learning: Regression

• Regression Types 101
• Univariate Regression 101
• Univariate Regression Interactive
• Univariate Regression Lab
• Univariate Regression Exercise
• Univariate Regression Solution
• Polynomial Regression 101
• Polynomial Regression Lab
• Multivariate Regression 101
• Multivariate Regression Lab
• Multivariate Regression Exercise
• Multivariate Regression Solution

10. Machine Learning: Model Preparation and Evaluation

• Underfitting / Overfitting 101
• Train / Validation / Test Split 101
• Train / Validation / Test Split Interactive
• Train / Validation / Test Split Lab
• Resampling Techniques 101
• Resampling Techniques Lab

11. Machine Learning: Regularization

• Regularization 101
• Regularization Lab

12. Machine Learning: Classification Basics

• Confusion Matrix 101
• ROC Curve 101
• ROC Curve Interactive
• ROC Curve Lab Introduction
• ROC Curve Lab 1/3 (Data Prep, Modeling)
• ROC Curve Lab 2/3 (Confusion Matrix and ROC)
• ROC Curve Lab 3/3 (ROC, AUC, Cost Function)

13. Machine Learning: Classification with Decision Trees

• Decision Trees 101
• Decision Trees Lab (Introduction)
• Decision Trees Lab (Coding)
• Decision Trees Exercise

14. Machine Learning: Classification with Random Forests

• Random Forests 101
• Random Forests Interactive
• Random Forest Lab (Introduction)
• Random Forest Lab (Coding 1/2)
• Random Forest Lab (Coding 2/2)

15. Machine Learning: Classification with Logistic Regression

• Logistic Regression 101
• Logistic Regression Lab (Introduction)
• Logistic Regression Lab (Coding 1/2)
• Logistic Regression Lab (Coding 2/2)
• Logistic Regression Exercise

16. Machine Learning: Classification with Support Vector Machines

• Support Vector Machines 101
• Support Vector Machines Lab (Introduction)
• Support Vector Machines Lab (Coding 1/2)
• Support Vector Machines Lab (Coding 2/2)
• Support Vector Machines Exercise

17. Machine Learning: Classification with Ensemble Models

• Ensemble Models 101

18. Machine Learning: Association Rules

• Association Rules 101
• Apriori 101
• Apriori Lab (Introduction)
• Apriori Lab (Coding 1/2)
• Apriori Lab (Coding 2/2)
• Apriori Exercise
• Apriori Solution

19. Machine Learning: Clustering

• Clustering Overview
• kmeans 101
• kmeans Lab
• kmeans Exercise
• kmeans Solution
• Hierarchical Clustering 101
• Hierarchical Clustering Interactive
• Hierarchical Clustering Lab
• DBSCAN 101
• DBSCAN Lab

20. Machine Learning: Dimensionality Reduction

• PCA 101
• PCA Lab
• PCA Exercise
• PCA Solution
• t-SNE 101
• t-SNE Lab (Sphere)
• t-SNE Lab (MNIST)
• Factor Analysis 101
• Factor Analysis Lab (Introduction)
• Factor Analysis Lab (Coding 1/2)
• Factor Analysis Lab (Coding 2/2)
• Factor Analysis Exercise

21. Machine Learning: Reinforcement Learning

• Reinforcement Learning 101
• Upper Confidence Bound 101
• Upper Confidence Bound Interactive
• Upper Confidence Bound Lab (Introduction)
• Upper Confidence Bound Lab (Coding 1/2)
• Upper Confidence Bound Lab (Coding 2/2)

22. Deep Learning: Introduction

• Deep Learning General Overview
• Deep Learning Modeling 101
• Performance
• From Perceptron to Neural Networks
• Layer Types
• Activation Functions
• Loss Function
• Optimizer
• Deep Learning Frameworks
• Python and Keras Installation

23. Deep Learning: Regression

• Multi-Target Regression Lab (Introduction)
• Multi-Target Regression Lab (Coding 1/2)
• Multi-Target Regression Lab (Coding 2/2)

24. Deep Learning: Classification

• Binary Classification Lab (Introduction)
• Binary Classification Lab (Coding 1/2)
• Binary Classification Lab (Coding 2/2)
• Multi-Label Classification Lab (Introduction)
• Multi-Label Classification Lab (Coding 1/3)
• Multi-Label Classification Lab (Coding 2/3)
• Multi-Label Classification Lab (Coding 3/3)

25. Deep Learning: Convolutional Neural Networks

• Convolutional Neural Networks 101
• Convolutional Neural Networks Interactive
• Convolutional Neural Networks Lab (Introduction)
• Convolutional Neural Networks Lab (1/1)
• Convolutional Neural Networks Exercise
• Semantic Segmentation 101
• Semantic Segmentation Lab (Introduction)
• Semantic Segmentation Lab (1/1)

26. Deep Learning: Autoencoders

• Autoencoders 101
• Autoencoders Lab (Introduction)
• Autoencoders Lab (Coding)

27. Deep Learning: Transfer Learning and Pretrained Networks

• Transfer Learning and Pretrained Models 101
• Transfer Learning and Pretrained Models Lab (Introduction)
• Transfer Learning and Pretrained Models Lab (1/1)

28. Deep Learning: Recurrent Neural Networks

• Recurrent Neural Networks 101
• LSTM: Univariate, Multistep Timeseries Prediction (Introduction)
• LSTM: Univariate, Multistep Timeseries Prediction Lab (1/1)
• LSTM: Multivariate, Multistep Timeseries Prediction (Introduction)
• LSTM: Multivariate, Multistep Timeseries Prediction Lab (1/1)

29. Shiny

• Shiny Introduction
• Popular Languages (Introduction)
• Popular Languages (global.R)
• Popular Languages (ui.R)
• Popular Languages (server.R)
• Reactive Expressions (101)
• Popular Languages (Reactive Expressions)
• App Deployment
• GDP and Life Expectancy (Exercise)
• GDP and Life Expectancy (Solution)