In various formats, one of the most frequent questions I am asked is the equivalent of:<\/p>\n “Can you recommend a free self-paced learning path for #machinelearning and #deeplearning?”<\/em><\/strong><\/p>\n In this post, I attempt an answer<\/p>\n This is based on my work \/ teaching students primarily at Oxford University, but I have chosen only free resources here i.e. publicly available.<\/p>\n Usual disclaimers apply i.e. the views are my own<\/p>\n Also. I would encourage you to support the authors by buying paid versions of their books if you can (I do so)<\/p>\n The challenge in creating such a learning path is:<\/p>\n So, my suggestion is: Use this learning pathway as a guide but shorten it as you want.<\/p>\n Try to go on a series of small journeys – each of which you will complete.<\/p>\n But overall, try and maintain the sequence and these resources (trust me between them – I don’t think you will miss anything!)<\/p>\n So, the first resource is a book: Python Data Science Handbook – by Jake VanderPlas<\/strong><\/p>\n The whole book is free on github<\/a> and it’s a relatively easy book to read<\/p>\n Covers the following topics<\/p>\n Once you have gone through this book, you will know machine learning (but not deep learning)<\/p>\n 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<\/p>\n The book is Machine Learning with Python cookbook by Chris Albon<\/a><\/p>\n The website chrisalbon.com<\/a> and the sequence of code I recommend is as below<\/p>\n I like this format because it fits in the deliberate practise approach of learning<\/a> i.e. lots of small things practised individually <\/p>\n Finally, two more resources.<\/p>\n So, coming back to the details of the second resource from the website chrisalbon.com<\/a> and the sequence of code I recommend is as below<\/p>\n If you liked this post, please follow me on linkedin Ajit Jaokar<\/a><\/p>\n Image source: jooinn<\/a><\/p>\n Machine Learning<\/strong><\/p>\n Basics<\/p>\n Vectors, Matrices, And Arrays<\/p>\n Preprocessing Structured Data<\/p>\n<\/a><\/p>\n<\/p>\n
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1. IPython: Beyond Normal Python<\/a><\/h3>\n
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2. Introduction to NumPy<\/a><\/h3>\n
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3. Data Manipulation with Pandas<\/a><\/h3>\n
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4. Visualization with Matplotlib<\/a><\/h3>\n
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5. Machine Learning<\/a><\/h3>\n
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