A free self-paced learning path for #machinelearning and #deeplearning

Author: ajit jaokar

In various formats, one of the most frequent questions I am asked is the equivalent of:

“Can you recommend a free self-paced learning path for #machinelearning and #deeplearning?”

In this post, I attempt an answer

This is based on my work / teaching students primarily at Oxford University, but I have chosen only free resources here i.e. publicly available.

Usual disclaimers apply i.e. the views are my own

Also. I would encourage you to support the authors by buying paid versions of their books if you can (I do so)

The challenge in creating such a learning path is:

  • It needs to be selective – because there is a lot of excellent content on the web – but from a learning standpoint – that can be overwhelming
  • You need to know a sequence. I provide a sequence below from experience of teaching
  • You need an end-point else you are not motivated to stay with it and you will drop out

So, my suggestion is: Use this learning pathway as a guide but shorten it as you want.

Try to go on a series of small journeys – each of which you will complete.

But overall, try and maintain the sequence and these resources (trust me between them – I don’t think you will miss anything!)

So, the first resource is a book: Python Data Science Handbook –  by Jake VanderPlas

The whole book is free on github and it’s a relatively easy book to read

Covers the following topics

1. IPython: Beyond Normal Python

2. Introduction to NumPy

3. Data Manipulation with Pandas

4. Visualization with Matplotlib

5. Machine Learning

Once you have gone through this book, you will know machine learning (but not deep learning)

So, the second resource is not a book i.e. the book is a paid book (which I recommend you buy) but the author’s web site has extensive code which you can run in small ‘cook book’ formats

The book is Machine Learning with Python cookbook by Chris Albon

The website chrisalbon.com and the sequence of code I recommend is as below

I like this format because it fits in the deliberate practise approach of learning i.e. lots of small things practised individually 

Finally, two more resources.

So, coming back to the details of the second resource from the website chrisalbon.com and the sequence of code I recommend is as below

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Image source: jooinn

Machine Learning


Vectors, Matrices, And Arrays

Preprocessing Structured Data

Preprocessing Images

Preprocessing Text

Preprocessing Dates And Times

Feature Engineering

Feature Selection

Model Evaluation

Model Selection

Linear Regression

Logistic Regression

Trees And Forests

Nearest Neighbors

Support Vector Machines

Naive Bayes


Deep Learning


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