Course Topics

Level 1 : Technical and Non-Technical Community

Chapter 1 – Introduction

  • Overview
  • History, Advantages and Disadvantages of AI
  • AI, Machine Learning and Deep Learning
Summary and Interactive Quiz

Chapter 2 – Types of Machine Learning

  • Supervised, Semi-Supervised, Un-Supervised ML
  • Classification, Regression and Clustering in ML
Summary and Interactive Quiz

Chapter 3 – Linear Regression

  • Introduction
  • Assumptions of Linear Regression
  • Implementing Linear Regression in Python
  • Error Calculation
Summary and Interactive Quiz

Chapter 4 – Logistic Regression

  • Introduction
  • How is it different from Linear Regression
  • Gradient Descent
  • Implementing Logistic Regression in Python
Summary and Interactive Quiz

Chapter 5 - Support Vector Machines

  • Introduction
  • How does SVM work?
  • Implementing SVM in Python
Summary and Interactive Quiz

Chapter 6 - Neural Network

  • Neural Network Basics
  • Forward feed and Back Propagation
  • Implementing Neural network
Summary and Interactive Quiz

Chapter 7 - Convolutional Neural Network

  • Overview
  • Use of CNN
  • Implementing CNN
Summary and Interactive Quiz

Chapter 8 - Recommender Systems

  • Introduction
  • Types of Recommender System
  • Implementing a Recommender System in python
Summary and Interactive Quiz

Chapter 9 - ML Tools and Platforms

  • TensorFlow
  • Scikit-Learn
  • Microsoft Azure ML
  • Google Cloud ML
  • Amazon Machine Learning
Summary and Interactive Quiz

Chapter 10 - Mini Assignment

Assignment to be done offline and submitted to the trainer for evaluation and feedback.