29 Chapters

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

R Programming

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

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

Hierarchical Clustering 101

Hierarchical Clustering Interactive

Hierarchical Clustering Lab

DBSCAN 101

DBSCAN Lab

Machine Learning: Dimensionality Reduction

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

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)

Transfer Learning and Pretrained Models Lab (1/1)

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)

Shiny

Shiny Introduction

Popular Languages (Introduction)

Popular Languages (global.R)

Popular Languages (ui.R)

Popular Languages (server.R)

Reactive Expressions (101)

App Deployment

GDP and Life Expectancy (Exercise)

GDP and Life Expectancy (Solution)

R Ultimate 2024 - R for Data Science and Machine Learning: Master R for Data Science, Visualization, and Machine Learning in 2024

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