Machine Learning in Python for Absolute Beginners

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Course Introduction

Chapter 2: Introduction to Machine Learning with Python

Lecture 1: Section Introduction

Lecture 2: What is Machine Learning?

Lecture 3: Types of Machine Learning

Lecture 4: Applications of Machine Learning

Lecture 5: Setting up the dev environment

Lecture 6: Summary

Chapter 3: Data Preparation

Lecture 1: Section Introduction

Lecture 2: Loading data sets

Lecture 3: Preprocessing text data

Lecture 4: Data Cleaning

Lecture 5: Handling the Missing Data

Lecture 6: Handling the Noisy Data

Lecture 7: Data Transformation

Lecture 8: Data Reduction

Lecture 9: Data Integration

Lecture 10: Summary

Chapter 4: Evaluation and tuning of Classification Models

Lecture 1: Section Introduction

Lecture 2: Introduction to Validation Techniques

Lecture 3: Resubstitution and Hold-out

Lecture 4: K-fold Cross-Validation

Lecture 5: Leave-One-Out-Cross-Validation

Lecture 6: Random Sub- Sampling and Bootstrapping

Lecture 7: Bias

Lecture 8: Variance

Lecture 9: Underfitting and Overfitting

Lecture 10: Hyperparameter tuning

Lecture 11: Implementing Hyperparameter Tuning

Lecture 12: Visualizing model results

Lecture 13: Summary

Chapter 5: Supervised Learning 1 – Regression

Lecture 1: Section Introduction

Lecture 2: Introduction to Linear regression

Lecture 3: Model evaluation and interpretation of results

Lecture 4: Important Metrics

Lecture 5: Confusion Matrix

Lecture 6: Multiple Linear regression

Lecture 7: Non linear regression

Lecture 8: Regression on Iris Data Set

Lecture 9: Summary

Chapter 6: Supervised Learning 2- Classification

Lecture 1: Section 5 Introduction

Lecture 2: Introduction to Classification algorithms – Part 1

Lecture 3: Introduction to Classification algorithms – Part 2

Lecture 4: Different types of Classification Algorithms

Lecture 5: Coding up a simple classification model using Decision Trees

Lecture 6: Coding up a simple classification model using Naive Bayes

Lecture 7: Summary

Chapter 7: Supervised Learning 3- Classification

Lecture 1: Section Introduction

Lecture 2: Introduction to Logistic Regression – Logistic vs. Linear Regression

Lecture 3: Introduction to Random Forest models

Lecture 4: Coding up a simple classification model using random forest

Lecture 5: Coding up a simple classification model using logistic regression

Lecture 6: Summary

Chapter 8: Supervised Learning 4 – Classification

Lecture 1: Section Introduction

Lecture 2: Introduction to K-nearest neighbors

Lecture 3: Introduction to SVM

Lecture 4: Coding up models using k-nearest neighbors

Lecture 5: Coding up models using SVM

Lecture 6: Summary

Chapter 9: Case studies from real world companies

Lecture 1: Case studies from real world companies -Part 1

Lecture 2: Case studies from real world companies -Part 2

Lecture 3: Credit Card Fraud Case

Lecture 4: Traffic prediction using machine learning

Lecture 5: Customer Behavior Analysis

Lecture 6: Fake news detection

Lecture 7: Summary