Machine Learning With Python: A Hands-on Introduction

Machine Learning: How and Why

UNIT I: DATA EXPLORATION AND VISUALISATION

1. Data Analytics: Statistics

2. Exploratory Data Analysis and Data Preparation

UNIT II: SUPERVISED MACHINE LEARNING - I

3. Linear Regression

4. Polynomial Regression and Regularisation

5. Tree-Based Methods

UNIT III: NEURAL NETWORKS

6. Neural Networks

UNIT IV: SUPERVISED MACHINE LEARNING - II

Interlude A: Image Denoising using a Simple Method

7. Naive Bayes: A Probabilistic Bayesian Approach to Classification

8. Classification

UNIT V: UNSUPERVISED MACHINE LEARNING

9. Unsupervised Learning I: Clustering

10. Unsupervised Learning II: Principal Components & Factor Analysis

Appendix A to G

Appendix-A: Introduction to Python

Appendix-B: Data Wrangling and Analysis with Pandas and NumPy

Appendix-C: Data Visualisation: Matplotlib, Seaborn, Plotly

Appendix-D: Data and Code Resources

Appendix-E: Some Crucial Python Libraries

Appendix-F: Content-Based Recommender Systems

Appendix-G: Collaborative Filtering: A Gateway to Personalised Recommendations

What Next?

Bibliography