From 749dc31e288ef29ad9341f3d857561d9b867c77b Mon Sep 17 00:00:00 2001 From: Chakib Benziane Date: Wed, 8 Feb 2017 12:40:48 +0100 Subject: [PATCH] Update README.md --- README.md | 42 +++++++----------------------------------- 1 file changed, 7 insertions(+), 35 deletions(-) diff --git a/README.md b/README.md index f761d78..92b1a2d 100644 --- a/README.md +++ b/README.md @@ -1,41 +1,19 @@ -# Top-down learning path: Machine Learning for Software Engineers - -

- - Top-down learning path: Machine Learning for Software Engineers - - - GitHub stars - - - GitHub forks - -

- -Inspired by [Google Interview University](https://github.com/jwasham/google-interview-university). +# Top-down learning path and resources: Machine Learning for Software Engineers -Translations: [Brazilian Portuguese](https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-pt-BR.md) -## What is it? +Inspired by [Machine Learning for Software Engineers +](https://github.com/ZuzooVn/machine-learning-for-software-engineers) by [Google Interview University](https://github.com/jwasham/google-interview-university). -This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer. +Translations: [Brazilian Portuguese](https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README-pt-BR.md) -My main goal was to find an approach to studying Machine Learning that is mainly hands-on and abstracts most of the math for the beginner. -This approach is unconventional because it’s the top-down and results-first approach designed for software engineers. -Please, feel free to make any contributions you feel will make it better. - ---- ## Table of Contents -- [What is it?](#what-is-it) - [Sides of machine learning?](#sides-of-machine-learning) - [Don't feel you aren't smart enough](#dont-feel-you-arent-smart-enough) - [About Video Resources](#about-video-resources) - [Prerequisite Knowledge](#prerequisite-knowledge) -- [The Daily Plan](#the-daily-plan) -- [Motivation](#motivation) - [Machine learning overview](#machine-learning-overview) - [Machine learning mastery](#machine-learning-mastery) - [Machine learning is fun](#machine-learning-is-fun) @@ -84,15 +62,6 @@ This short section were prerequisites/interesting info I wanted to learn before - [x] [Don’t Break The Chain](http://lifehacker.com/281626/jerry-seinfelds-productivity-secret) - [x] [How to learn on your own](https://metacademy.org/roadmaps/rgrosse/learn_on_your_own) -## The Daily Plan - -Each subject does not require a whole day to be able to understand it fully, and you can do multiple of these in a day. - -Each day I take one subject from the list below, read it cover to cover, take note, do the exercises and write an implementation in Python or R. - -# Motivation -- [x] [Dream](https://www.youtube.com/watch?v=g-jwWYX7Jlo) - ## Math Fundamentals - [ ] [Coding the Matrix](http://codingthematrix.com) [torrent](http://academictorrents.com/details/54cd86f3038dfd446b037891406ba4e0b1200d5a) - [ ] Coursera introduction to statistics 101 @@ -159,6 +128,9 @@ Each day I take one subject from the list below, read it cover to cover, take no - [ ] [The 10 Algorithms Machine Learning Engineers Need to Know](https://gab41.lab41.org/the-10-algorithms-machine-learning-engineers-need-to-know-f4bb63f5b2fa#.ofc7t2965) - [ ] [Comparing supervised learning algorithms](http://www.dataschool.io/comparing-supervised-learning-algorithms/) +## Neural Networks for NLP +- [ ] [i am trask](http://iamtrask.github.io) + ## Beginner Books - [ ] [Data Smart: Using Data Science to Transform Information into Insight 1st Edition](https://www.amazon.com/Data-Smart-Science-Transform-Information/dp/111866146X) - [ ] [Data Science for Business: What you need to know about data mining and data­ analytic-thinking](https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/)