Machine Learning – Regression and Classification (math Inc.)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Install anaconda on your machine

Lecture 3: Set up environment and Download Machine Learning Libraries

Lecture 4: Introduction to Jupyter Notebook

Lecture 5: Introduction to Artificial Intelligence and Machine Learning [lecture]

Chapter 2: Introduction to Machine Learning libraries of Python [Data preprocessing]

Lecture 1: Key terms in Machine Learning [supervised, unsupervised,, classification..]

Lecture 2: Data Types in Machine Learning

Lecture 3: Structured Data sets used in Machine Learning

Lecture 4: Data Preprocessing Part 1

Lecture 5: Data Preprocessing Part 2

Lecture 6: Data Preprocessing Part 3

Lecture 7: Introduction to numpy module

Lecture 8: Introduction to pandas module

Lecture 9: Encoding Process in Machine Learning

Lecture 10: Train and Test Splitting of Data

Lecture 11: Key Terms used in Machine Learning [dimensionality, underfitting, overfitting]

Chapter 3: Decision Trees [ Information Gain and GINI impurity ]

Lecture 1: Lecture: Learn about Information Gain algorithm

Lecture 2: Lecture: Decision Tree Classifier, Which split is better?

Lecture 3: Tutorial: Implement decision tree numerical using Information Gain

Lecture 4: Tutorial continue: Implement decision Tree numerical

Lecture 5: Lecture: Learn about GINI impurity algorithm

Lecture 6: Workshop: Code Decision Tree Classifier

Lecture 7: Workshop continue: Code Decision Tree Classifier

Lecture 8: Coding Confusion matrix: Implement DTC [Decision Tree Classifier]

Lecture 9: Lecture: Learn about regression Trees

Lecture 10: Lecture: Learn about creation of Regression Trees

Lecture 11: Lecture: Continue to built Regression Tree [Sum of Squared Residuals(Variance) ]

Lecture 12: Lecture: Find optimal decision using Regression Tree

Lecture 13: Workshop: Code Decision Tree Regressor

Chapter 4: Linear Regression [Simple, Multiple and Polynomial Regression]

Lecture 1: Lecture: Intro to Linear Regression

Lecture 2: Lecture: Learn about OLS [Ordinary Least Squares] algorithm

Lecture 3: Lecture: Introduction to working of Linear Regression

Lecture 4: Lecture: Introduction to MSE, MAE, RMSE

Lecture 5: Lecture: Introduction to R squared

Lecture 6: Tutorial: Implement Simple linear regression numerical [calculate best fit line]

Lecture 7: Workshop: Implement Simple Linear Regression

Lecture 8: Lecture: Difference between Simple and Multiple Regression

Lecture 9: Workshop: Implement Multiple Linear Regression

Lecture 10: Workshop: Implement Multiple Linear Regression

Lecture 11: Workshop: Implement Polynomial Regression

Lecture 12: Workshop continue: Implement Polynomial Regression

Chapter 5: Multivariate Linear Regression

Lecture 1: Lecture: Learn about multivariate regression

Lecture 2: Lecture + tutorial: Compute the partial derivative using Gradient Descent

Lecture 3: Workshop: Implement Multivariate Regression

Lecture 4: Workshop: Compute cost using Loss function

Lecture 5: Workshop: Implement Gradient Descent for Multi-variate Regression

Chapter 6: Logistic Regression

Lecture 1: Lecture: Learn about Logistic Regression

Lecture 2: Lecture: Learn about hypothetical function [sigmoid/logit function]

Lecture 3: Lecture: Logistic Math Overview

Lecture 4: Lecture: Learn about decision boundary

Lecture 5: Lecture: Learn about Cost function of Logistic Regression

Lecture 6: Lecture: Learn about Gradient Descent

Lecture 7: Workshop: Implement Logistic Regression

Lecture 8: Workshop final: Implement Logistic Regression