A Mathematical and Programming Course on Machine Learning

Course Curriculum

Chapter 1: A mathematical approach to Machine learning : An Overview

Lecture 1: Detail Overview of Course

Lecture 2: How to connect Google CoLab with .csv file

Lecture 3: Basics of Python : Part1(Basic Constructs, IF Else)

Lecture 4: Basics of Python : Part2(Loops in python)

Lecture 5: Basics of Python : Part3(List)

Lecture 6: Basics of Python : Part4(Dictionary and Tuples)

Lecture 7: Basics of Python : Part5(Functions)

Lecture 8: Concepts of Numpy in Google Colab

Lecture 9: Concepts of Pandas part1

Lecture 10: Concepts of Pandas part2

Lecture 11: Exploratory data analysis using test case-part1

Lecture 12: Data Cleaning using Test case part-2

Chapter 2: Statistics using Python

Lecture 1: Descriptive Statistics and its importance

Lecture 2: Different types of Probability Distributions

Chapter 3: Machine Learning using Python

Lecture 1: Different Types of Learning

Lecture 2: Bayesian Learning and NB classifier

Lecture 3: Naïve Bayes Classifier

Lecture 4: Bayesian Network

Lecture 5: Implementation of Naïve Bayesian Classifier in Python

Lecture 6: Practical Implementation of Naïve Bayes Classifier

Lecture 7: Concept of different type of Regularization

Lecture 8: Regularization Theory : Practical Implementation of Ridge and Lasso Regression

Lecture 9: Principal Component Analysis and LDA : part1

Lecture 10: Principal Component Analysis and LDA : part2

Lecture 11: Implementation of Linear and Quadratic Discriminant Analysis

Lecture 12: Building PCA from Scratch using Python

Lecture 13: KNN Algorithm using Python

Chapter 4: Linear Regression and Logistic Regression using Python

Lecture 1: Concept of Linear and Logistic Regression

Lecture 2: Implementation of linear regression from scratch

Lecture 3: An example of Ordinary Least Square

Lecture 4: Introduction to Multiple Linear Regression

Lecture 5: Importance of significance test in multiple linear regression

Lecture 6: Practical Implementation of Multiple Linear regression

Lecture 7: Maximum Likelihood estimation importance in Logistic Regression

Lecture 8: Logistic Regression and analysis

Lecture 9: Implementation of Logistic Regression in python

Lecture 10: Loan Prediction algorithm using Logistic Regression

Lecture 11: Feature Engineering on continuous data : House Prediction

Lecture 12: Feature Engineering on Categorical Variable : House Prediction

Chapter 5: Decision Trees and its implementation in Python

Lecture 1: Overview of Section 4 Decision Tree and its implementation

Lecture 2: Introduction to Decision tree and ID3

Lecture 3: Introduction to ID3 and its concepts

Lecture 4: Concept of Entropy, Overfitting and Information Gain

Lecture 5: Practical Implementation of ID3and their limitations

Lecture 6: C4.5 Decision and its advancement over ID3 Decision tree

Lecture 7: Building a Decision tree with data using python from scratch

Lecture 8: Practical Implementation of CART Algorithm using Python

Lecture 9: CHAID Algorithm and its importance in Data Analytics

Lecture 10: Implementation of CHAID using Python

Chapter 6: Recommendation System using Python

Lecture 1: Introduction to Recommendation System (RecSys)

Lecture 2: Memory based Collaborative Filtering (CF)

Lecture 3: Matrix Decomposition based Collaborative Filtering

Lecture 4: Item based Recommendation System

Lecture 5: Movie Recommendation using Content Based Filtering

Lecture 6: Collaborative Filtering based Movie Recommendation

Lecture 7: Hybrid Collaborative filtering

Lecture 8: Implementation of clustering in Recommendation System

Lecture 9: Implementation of Machine Learning in Recommendation System

Chapter 7: Support Vector Machines and its types

Lecture 1: Introduction to Support Vector Machines

Lecture 2: The Constrained Optimization Problem

Lecture 3: The dual formulation concept of SVM part1

Lecture 4: The dual formulation concept of SVM part2

Lecture 5: Maximum margin with the introduction of noise

Lecture 6: Non Linear SVM Classifier

Lecture 7: Implementation of Linear and Non Linear Support Vector Classifier

Lecture 8: Implementation of SVR(Support Vector Regressor

Lecture 9: Multi SVM and their applications

Lecture 10: Case Study: Implementation of Support Vector Classifier

Lecture 11: Case Study : Implementation of Support Vector Regressor

Chapter 8: Neural Network and Deep Neural Network

Lecture 1: Introduction of Tensor flow in Neural Network and Deep Networks

Lecture 2: Data representation in the form of Tensors

Lecture 3: Concept of Artificial Neural Network

Lecture 4: The Perceptron

Lecture 5: Practical implementation of Perceptron

Lecture 6: Gradient Descent Algorithm using python

Lecture 7: Stochastic gradient Descent Algorithm using Python

Lecture 8: Mini Batch Gradient Descent Algorithm

Lecture 9: Practical implementation of MLP

Lecture 10: Back Propagation Algorithm

Lecture 11: Batch Normalization

Lecture 12: Building a Classifier for Fashion MNIST dataset

Lecture 13: Implementation of Linear regression using Tensor flow on Housing data

Lecture 14: Practical implementation of Convolution Neural Network

Lecture 15: A brief introduction of Computer vision using openCV

Chapter 9: Un Supervised Learning and Concepts of Clustering

Lecture 1: Introduction to the concepts of Unsupervised Learning and clustering

Lecture 2: The Mathematics of K-Means Clustering