| Week | Primary Topic | Description | Coursera Topic | Hands-On ML Book Chapters |
|---|---|---|---|---|
| 1 | Overview | Overview, Google Colab, Python Refresher | Week 1 | Chapter 1 |
| 2 | Regression- part 1 | Overview, Hands-on Jupyter Notebook | Week 1 | Chapter 4 |
| 3 | Regression - part 2 | Quality/performance metrics, Normalization/feature scaling, polynomial regression, logistic regression , decision trees | week 2, week 3 | Chapter 2 ,Chapter 6 |
| 4 | Feature Scaling, Regression Model Evaluation | |||
| 5 | Classification | Chapter 3 | ||
| 6 | Neural Networks Learning | Chapter 10 | ||
| 7 | Deep Neural Networks | Chapter 11 | ||
| 8 | K-means Clustering | Overview of clustering | Lecture 13 | Chapter 9 |
| 9 | Reinforcement Learning | Chapter 18 | ||
| 10 | Advance Reinforcement Learning | |||
| 11 | Autoencoders | |||
| 12 | VAE & GAN |
Summary: this week was an introduction to ML. We covered the following topics.
- What is machine learning?
- What are the broad types of machine learning?
- Jupyter notebooks with Google Colab
Reference materials:
Homework for the next class:
- Review the Python Refresher
- Review the Jupyter Notebook for Python
- For a more detailed overview of ML, read chapter 1 of the Hands-on ML Book
- Watch the week 1 videos on the Coursera Machine Learning Course
Summary: This week we will cover:
- Regression
- Linear Regression
- Polynomial Regression
- Training Data - Test/Train Split
- Hands-On Activity:
- Linear Regression
- Boston Housing Data
Homework and reading for the next class:
- The "Select a Performance Measure" section from chapter 2 of the book.
- The "Feature Scaling" section from chapter 2 of the book.
- The "Multivariate Linear Regression" videos/readings from week 2 of Coursera for understanding polynomial regression.
- The "Classification and Representation" videos/readings from week 3 of Coursera for understanding logistic regression.
Summary: This week we will cover:
- Feature Scaling
- Normalization
- Standardization
- Regression Model Evaluation
Summary: This week we will cover:
- Classification
- Encoding
- Different classification methods using Scikit
Summary: This week we will cover topics around neural networks
Summary: This week we will cover topics around deep learning and CNN.
Summary: This week we will cover Clustering and K-means clustering
Summary: This week we will cover Reinforcement Learning
Summary we cover more concepts in Reinforcement Learning
Summary: In this lecture we covered the Autoencoders.
Summary : In this lecture we will cover the Variational Autoencoder(VAE) and Generative Adversarial Networks (GAN)