Table of Contents
- Introduction to Machine Learning
- Statistical Analysis
- Linear Regression
- Logistic Regression
- Decision Trees
- Random Forest
- Rule-Based Classifiers
- Naïve Bayesian Classifier
- K-Nearest Neighbors Classifiers
- Support Vector Machine
- K-Means Clustering
- Dimensionality Reduction
- Association Rules Mining and FP Growth
- Reinforcement Learning
- Applications of ML Algorithms
- Applications of Deep Learning
- Advance Topics and Future Directions