MACHINE LEARNING FOUNDATION

Machine Learning Introduction

What is Machine Learning

Applications of Machine Learning

Machine Learning vs Artificial Intelligence

Machine Learning Languages and platforms

Machine Learning vs Statistical Modelling

Machine Learning Algorithms

Popular Machine Learning Algorithms

Clustering, Classification and Regression

Supervised vs Unsupervised Learning

Application of Supervised Learning Algorithms

Application of Unsupervised Learning Algorithms

Overview of modeling Machine Learning Algorithm: Train, Evaluation and Testing.

How to choose Machine Learning Algorithm?

Supervised Learning I

Simple Linear Regression: Theory, Implementing in Python (and R), Working on use case.

Multiple Linear Regression: Theory, Implementing in Python (and R),

Working on use case.

K-Nearest Neighbors: Theory, Implementing in Python (and R), KNN advantages, Working on use case.

Decision Trees: Theory, Implementing in Python (and R), Decision |Tree Pros and Cons, Working on use case.

Random Forests: Theory, Implementing in Python (and R), Reliability of Random Forests, Working on Use Case.

Supervised Learning II

Naive Bayes Classifier: Theory, Implementing in Python (and R), Why Naive Bayes is simple yet powerful, Working on use case.

Support Vector Machines: Theory, Support vector machines with Python and R, Improving the performance with Kernals, Working on Use Case.

Association Rules: Theory, Implementing in Python (and R), Working on use case.

Model Evaluation: Overfitting & Underfitting

Understanding Different Evaluation Models

Unsupervised Learning

K-Means Clustering: Theory, Euclidean Distance method.

K-Means hands on with Python (and R)

K-Means Advantages & Disadvantages

Hierarchical Clustering: Theory

Hierarchical Clustering with Python (and R)

Hierarchical Advantages & Disadvantages

Dimensionality Reduction

Dimensionality Reduction: Feature Extraction & Selection

Principal Component Analysis (PCA): Theory, Eigen Vectors

PCA example with Python (and R) with Use case

Advantages of Dimensionality Reduction

Application of Dimensinality Reduction with case study.

Collaborative Filtering & Its Challenges