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:
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
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 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
Vectors, Matrices, And Arrays
Preprocessing Structured Data
Preprocessing Dates And Times
Trees And Forests
Support Vector Machines