NOTES OF

Hand on Machine Learning

with Scikit-Learn and TensorFlow

It is always good to go back to basics and revisit old concepts to rectify the fading concepts in our memory. In a similar attempt, I am going to make notes for my revisit to old concepts. The notes are made from Hands on Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron.

These notes are in no means replacement to the awesome book. These can be used as a companion or as revision guide.

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Tables Of Contents

PART-1-The Fundamentals of Machine Learning

1. The Machine Learning Landscape
   
2. End-to-End Machine Learning Project
   
3. Classification
   
4. Training Models
   
5. Support Vector Machines
   
6. Decision Trees
   
7. Ensemble Learning and Random Forests
   
8. Dimensionality Reduction
   

PART-2-Neural Networks And Deep Learning Learning

1. Up and Running With TensorFlow
   
2. Introduction to Artificial Neural Networks
   
3. Training Deep Neural Nets
   
4. Distributing TensorFlow between devices and servers
   
5. Convolutional Neural Networks
   
6. Recurrent Neural Networks
   
7. Autoencoders
   
8. Reinforcement Learning
   

Some Helpful Links

These are some helpful markdowns I put together while going through the book.

• Machine Learning Notations
• Workspace Setup
• Scikit-Learn API Design
• Bias variance tradeoff

There are some youtube channels to speed up this learning process:

• Aurélien Géron
• Two Minute Papers
• Sentdex
• TenorFlow
• Jeremy Howard
• Siraj Rawal