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# Top-down learning path: Machine Learning for Software Engineers
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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’
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’ ](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 ](https://www.amazon.com/Data-Science-Business-Data-Analytic-Thinking/dp/1449361323/ )
Chakib Benziane
7 years ago
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GitHub