Beginner to Advanced Guide on Machine Learning with R Tool

Course Curriculum

Chapter 1: Module-1 Introduction to Course

Lecture 1: 1.1 Introduction to the Course

Lecture 2: 1.2 Pre-Requisite

Lecture 3: 1.3 What you will Learn

Lecture 4: 1.4 Techniques of Machine Learning

Chapter 2: Module-2 Introduction to validation and its Methods

Lecture 1: 2.1 Introduction to Cross Validation

Lecture 2: 2.2 Cross Validation Method

Lecture 3: 2.3 Caret package

Chapter 3: Module-3 Classification

Lecture 1: 3.1 Introduction to Classification

Lecture 2: 3.2 KNN- K Nearest Neighbors

Lecture 3: 3.3 Implementation of KNN Algorithm

Lecture 4: 3.4 Naive-Bayes Classifier

Lecture 5: 3.5 Implementation of Naive-Bayes Classifier

Lecture 6: 3.6 Linear Discriminant Analysis

Lecture 7: 3.7 Implementation of Linear Discriminant Analysis

Chapter 4: Module-4 Black Box Method-Neural network and SVM

Lecture 1: 4.1 Introduction to Artificial Neural Network

Lecture 2: 4.2 Conceptualizing of Neural Network

Lecture 3: 4.3 Implement Neural Network in R

Lecture 4: 4.4 Back Propagation

Lecture 5: 4.5 Implementation of Back Propagation Network

Lecture 6: 4.6 Introduction to Support Vector Machine

Lecture 7: 4.7 Implementation of SVM in R

Chapter 5: Module-5 Tree Based Models

Lecture 1: 5.1 Decision Tree

Lecture 2: 5.2 Implementation of Decision Tree

Lecture 3: 5.3 Bagging

Lecture 4: 5.4 Boosting

Lecture 5: 5.5 Introduction to Random Forest

Lecture 6: 5.6 Implementation of Random Forest

Chapter 6: Module-6 Clustering

Lecture 1: 6.1 Introduction to Clustering

Lecture 2: 6.2 K-Means Clustering

Lecture 3: 6.3 Implementation of K-Means Clustering

Lecture 4: 6.4 Hierarchical Clustering

Chapter 7: Module-7 Regression

Lecture 1: 7.1 Predicting with Linear Regression

Lecture 2: 7.2 Implementation of Linear Regression

Lecture 3: 7.3 Multiple Covariates Regression

Lecture 4: 7.4 Logistic Regression

Lecture 5: 7.5 Implementation of Logistic Regression

Lecture 6: 7.6 Forecasting

Lecture 7: 7.7 Implementation of Forecasting