Machine Learning Notebooks

Welcome to the Machine Learning Notebooks!

Prerequisites (see below)

Notebooks

1. The Machine Learning landscape
2. End-to-end Machine Learning project
3. Classification
4. Training Linear Models
5. Support Vector Machines
6. Decision Trees
7. Ensemble Learning and Random Forests
8. Dimensionality Reduction & Unsupervised Learning
9. Up and running with TensorFlow
10. Introduction to Artificial Neural Networks
11. Deep Learning
12. Distributed TensorFlow
13. Convolutional Neural Networks
14. Recurrent Neural Networks
15. Autoencoders
16. Reinforcement Learning

Scientific Python tutorials

• NumPy
• Matplotlib
• Pandas

Math Tutorials

• Linear Algebra
• Differential Calculus

Extra Material

• Capsule Networks
• TensorFlow Reproducibility

Misc.

• Equations (list of equations in the book)

Prerequisites

To understand

• Python – you don't need to be an expert python programmer, but you do need to know the basics. If you don't, the official Python tutorial is a good place to start.
• Scientific Python – We will be using a few popular python libraries, in particular NumPy, matplotlib and pandas. If you are not familiar with these libraries, you should probably start by going through the tutorials in the Tools section (especially NumPy).
• Math – We will also use some notions of Linear Algebra, Calculus, Statistics and Probability theory. You should be able to follow along if you learned these in the past as it won't be very advanced, but if you don't know about these topics or you need a refresher then go through the appropriate introduction in the Math section.

To run the examples

• Jupyter – These notebooks are based on Jupyter. If you just plan to read without running any code, there's really nothing more to know, just keep reading! But if you want to experiment with the code examples you need to:
  • follow the installation instructions,
  • learn how to use Jupyter. Start the User Interface Tour from the Help menu.