R for Data Science and Machine Learning Online Course
R for Data Science and Machine Learning Online Course
R for Data Science and Machine Learning Online Course
About the Course
R is one of the most widely used programming languages among data scientists, analysts, and researchers for tasks such as data manipulation, statistical modeling, and visualization. Its flexibility and extensive ecosystem of tools make it especially powerful for data-driven analysis and applications.
In this course, you'll learn how to:
Master R fundamentals and advanced data science techniques
Transform, aggregate, and visualize data with precision
Create compelling visualizations using ggplot2, Plotly, and Leaflet
Implement machine learning models for regression, classification, and clustering
Explore advanced topics such as neural networks, image classification, and segmentation
Build interactive web applications using R Shiny to create dynamic user experiences
Who is this course for?
This course is ideal for learners of all levels—from beginners starting their data science journey to experienced professionals looking to enhance their skills in data analysis, visualization, and decision-making.
Course Table of Contents
Course Introduction
Course Overview
R and RStudio (Overview and Installation)
How to Get the Code?
RStudio Introduction / Project Setup
File Formats
Rmarkdown Lab
Data Types and Structures
Basic Data Types 101
Basic Data Types Lab
Matrices and Arrays Lab
Lists
Factors
Dataframes
Strings Lab
Datetime
R Programming
Operators
Loops 101
Loops Lab
Functions 101
Functions Lab (Introduction)
Functions Lab (Coding)
Data Import and Export
Data Import Lab
Data Export Lab
Web Scraping Introduction
Web Scraping Lab
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
Data Visualization
Visualization Overview
ggplot 101
ggplot Lab
plotly Lab (Introduction)
plotly Lab
leaflet Lab (Introduction)
leaflet Lab
dygraphs Lab (Introduction)
dygraphs Lab
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
Machine Learning: Introduction
AI 101
Machine Learning 101
Models
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
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
Machine Learning: Regularization
Regularization 101
Regularization Lab
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)
Machine Learning: Classification with Decision Trees
Decision Trees 101
Decision Trees Lab (Introduction)
Decision Trees Lab (Coding)
Decision Trees Exercise
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)
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
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
Machine Learning: Classification with Ensemble Models
Ensemble Models 101
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
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
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
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)
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
Deep Learning: Regression
Multi-Target Regression Lab (Introduction)
Multi-Target Regression Lab (Coding 1/2)
Multi-Target Regression Lab (Coding 2/2)
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)
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)
Deep Learning: Autoencoders
Autoencoders 101
Autoencoders Lab (Introduction)
Autoencoders Lab (Coding)
Deep Learning: Transfer Learning and Pretrained Networks
Transfer Learning and Pretrained Models 101
Transfer Learning and Pretrained Models Lab (Introduction)