In this post, I explain<\/p>\n \u00a0<\/p>\n To provide some context, I posted about the idea of learning coding for machine learning \/ deep learning in a weekend<\/a> We have had considerable success with this \u2013 and now we are planning the next stage.<\/p>\n \u00a0<\/p>\n To participate in this book series and to get the free books \u2013 please join this group<\/p>\n \u00a0<\/p>\n AI deeplearning machinelearning coding in a week<\/a><\/p>\n \u00a0<\/p>\n The\u00a0first book\u00a0 in this series is\u00a0<\/p>\n Coding in a weekend – Classification and Regression<\/strong> launched in Apr 2019<\/p>\n \u00a0<\/p>\n Each book will have\u00a0<\/p>\n forthcoming books in this series are (note the group will remain the same i.e.<\/strong> AI deeplearning machinelearning coding in a week<\/a>)<\/p>\n \u00a0<\/p>\n \u00a0<\/p>\n Created and managed by Ajit Jaokar, Dan Howarth, Ayse Mutlu – London UK. we welcome other community moderators. The books are free but exclusive to Data Science Central\u00a0<\/p>\n Finally, from our trials, here are five great free resources we recommend<\/p>\n 1) Python Data Science Handbook by Jake VanderPlas<\/a><\/p>\n 2)\u00a0Dive into Python 3<\/a><\/p>\n 3) Python crash course by Eric Matthes\u00a0<\/a><\/p>\n 4)\u00a0Python crash course by Eric Matthes cheat sheets<\/a><\/p>\n 5) Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning by Chris Albon<\/a><\/p>\n I highly recommend Chris Albon book \u2013 however the book itself is not free. But the site above provides code in lots of granular detail for the book and we found the code very useful (and we bought the book)<\/p>\n<\/p>\n Don\u2019t forget to join the group for the free books<\/span><\/p>\n AI deeplearning machinelearning coding in a week<\/a><\/span><\/p>\n \u00a0<\/p>\n Image source https:\/\/outreach.wikimedia.org\/wiki\/Bookshelf\/Wikipedia<\/a><\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/p>\n<\/p>\n
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