Machine Learning in Python for Professionals

Course Curriculum

Chapter 1: Course Overview

Lecture 1: Course Introduction

Chapter 2: Supervised Learning – Advanced Classification models

Lecture 1: Introduction

Lecture 2: Introduction to Ensemble Model

Lecture 3: Types of Ensemble Models – Bagging Model

Lecture 4: Types of Ensemble Models – Boosting Model

Lecture 5: Difference betweeen Bagging and Boosting Model

Lecture 6: Implementing Gradient Boosting Techniques

Lecture 7: Implementing Adaptive Boosting Technique

Lecture 8: Summary

Chapter 3: Unsupervised Learning

Lecture 1: Section Introduction

Lecture 2: Introduction to Unsupervised Learning

Lecture 3: Types of Clustering Techniques

Lecture 4: Introduction to K-means Clustering-1

Lecture 5: Introduction to K-means Clustering-2

Lecture 6: Determine the K-value in K-means Clustering

Lecture 7: Methods to Select K-value in K-means Clustering

Lecture 8: Implementing K-means Clustering Algorithm-1

Lecture 9: Implementing K-means Clustering Algorithm-2

Lecture 10: Optimizing K-means Algorithm

Lecture 11: Introduction to Hierarchical Clustering

Lecture 12: Compare Hierarchical Clustering

Lecture 13: Introduction to Divisive Hierarchical Clustering

Lecture 14: Summary

Chapter 4: Explainable Artificial Intelligence

Lecture 1: Section Introduction

Lecture 2: Introduction to Explainable Artificial Intelligence

Lecture 3: Need for Explainable AI

Lecture 4: Value of Explainable AI

Lecture 5: Techniques of Explainable

Lecture 6: Pros, Cons and Application – Shapley And Lime

Lecture 7: Challenges of Explainable AI

Lecture 8: Implementing XAI on Unsupervised Model

Lecture 9: Real Time Application of XAI

Lecture 10: Summary

Chapter 5: Dimensionality Reduction

Lecture 1: Section Introduction

Lecture 2: Introduction to Dimensionality Reduction

Lecture 3: Dimensionality Reduction – When and How

Lecture 4: Curse of Dimensionality

Lecture 5: Linear Methods of Dimensionality Reduction

Lecture 6: Introduction to Principal Component Analysis

Lecture 7: Principal Component Analysis – Advantages and Disadvantages

Lecture 8: Implementing PCA in Python

Lecture 9: Non-Linear Dimensionality Reduction – MDS

Lecture 10: Non-Linear Dimensionality Reduction – ISOMAP

Lecture 11: Non-Linear Dimensionality Reduction – t-SNE

Lecture 12: t-SNE – Pros, Cons and Application

Lecture 13: Summary

Chapter 6: Recommendation Systems

Lecture 1: Section Introduction

Lecture 2: What is Recommender System?

Lecture 3: Need for Recommender Systems

Lecture 4: Types of Recommender Models

Lecture 5: Content Based Recommendation System

Lecture 6: Working of Content Based Recommendation System – 1

Lecture 7: Working of Content Based Recommendation System – 2

Lecture 8: Types of Similarities – Content Based System

Lecture 9: Advantages and Disadvantages – Content Based System

Lecture 10: Implementing Content Based Recommender

Lecture 11: Collaborative Filtering Based Recommendation System

Lecture 12: Different Approaches in Collaborative Filtering

Lecture 13: Item Based Collaborative Filtering

Lecture 14: Matrix Factorization in Collaborative Filtering

Lecture 15: Advantages and Disadvantages – Collaborative Filtering

Lecture 16: Implementing Collaborative Filtering

Lecture 17: Difference Between Content and Collaborative Filtering

Lecture 18: Challenges with Recommendation System

Lecture 19: Summary

Chapter 7: Reinforcement Learning

Lecture 1: Section Introduction

Lecture 2: Introduction to Reinforcement Learning

Lecture 3: Need of Reinforcement Learning

Lecture 4: Components of Reinforcement Learning – 1

Lecture 5: Components of Reinforcement Learning – 2

Lecture 6: Q Learning Method – 1

Lecture 7: Q Learning Method – 2

Lecture 8: Types and Methods of Reinforcement Learning

Lecture 9: Advantages and Disadvantages of Reinforcement Learning

Lecture 10: Application of Reinforcement Learning

Lecture 11: Future of Reinforcement Learning

Lecture 12: Summary