Full Machine Learning with R

Topic 1 Overview of Machine Learning

Introduction to Machine Learning

Pattern Recognition Problems Suitable for Machine Learning

Supervised vs Unsupervised Learnings

Types of Machine Learning

Machine Learning Techniques

R Packages for Machine Learning

Topic 2 Regression

What is Regression

Applications of Regression

Least Square Error Minimization

Data Pre-processing

Bias vs Variance Trade-off

Regression Methods with Regularization

Topic 3 Classification

What is Classification

Applications of Classification

Classification Algorithms

Confusion Matrix

Classification Performance Evaluation

Topic 4 Clustering

What is Clustering

Applications of Clustering

Distance Measure

Clustering Algorithms

Clustering Performance Evaluation

Anomaly Detection Problem

Topic 5 Principal Component Analysis

Principal Component Analysis (PCA) and Dimension Reduction

Applications of PCA

PCA Workflow

Topic 6 Neural Network

What is Neural Network

Activation Functions

Deep Learning vs Machine Learning

Classification Using Neural Network

Topic 7 Ensemble Methods

Random Forest Ensemble

Gradient Boost and XGBoost Ensemble

Stacking Ensemble

Topic 8 Hyperparameter Tuning

Exhaustive Grid

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