Machine Learning: Making computers think!

Course Curriculum

Chapter 1: Introduction

Lecture 1: About Me

Lecture 2: Introduction to machine learning

Lecture 3: Supervised vs Unsupervised learning

Lecture 4: Setting up Environment

Lecture 5: Installing dependencies

Lecture 6: Setting up jupyter notebook

Chapter 2: Data Collection

Lecture 1: Data Fetching

Lecture 2: Data Importing

Chapter 3: Data Analysis

Lecture 1: Introduction

Lecture 2: Data Exploration

Lecture 3: Numerical vs categorical data

Lecture 4: Descriptive Statistics and Inferential Statistics

Lecture 5: Data Visualization

Lecture 6: Handling Outliers

Chapter 4: Data Preprocessing

Lecture 1: Introduction

Lecture 2: Standarization and Normalization

Lecture 3: Handling Categorical data

Lecture 4: Handling Missing Values

Chapter 5: Data Modelling

Lecture 1: Introduction

Lecture 2: Type of Algorithms

Lecture 3: Model in Action

Lecture 4: Feature Selection

Chapter 6: Model Validation

Lecture 1: Introduction

Lecture 2: Metrics for validation

Lecture 3: Trying Different Metrics

Lecture 4: Other Methods for Validation

Lecture 5: Overfit Vs. Underfit

Lecture 6: Methods to Improve Accuracy

Lecture 7: Hyperparameter Tuning

Chapter 7: Ensemble Learning

Lecture 1: Introduction

Lecture 2: Why Ensemble?

Lecture 3: Types of Ensembling Techniques

Lecture 4: Ensembling in Action

Chapter 8: Dimensionality Reduction

Lecture 1: Introduction

Lecture 2: Curse of Dimensionality

Lecture 3: Principal Component Analysis

Chapter 9: Outro

Lecture 1: Outro