Top-down learning path: Machine Learning for Software Engineers

Inspired by Google Interview University.

Translations: Brazilian Portuguese | 中文版本

What is it?

This is my multi-month study plan for going from mobile developer (self-taught, no CS degree) to machine learning engineer.

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?
Why use it?
How to use it
Follow me
Don't feel you aren't smart enough
About Video Resources
Prerequisite Knowledge
The Daily Plan
Motivation
Machine learning overview
Machine learning mastery
Machine learning is fun
Inky Machine Learning
Machine learning: an in-depth, non-technical guide
Stories and experiences
Machine Learning Algorithms
Beginner Books
Practical Books
Kaggle knowledge competitions
Video Series
MOOC
Resources
Becoming an Open Source Contributor
Games
Podcasts
Communities
Conferences
Interview Questions
My admired companies

Why use it?

I'm following this plan to prepare for my near-future job: Machine learning engineer. I've been building native mobile applications (Android/iOS/Blackberry) since 2011. I have a Software Engineering degree, not a Computer Science degree. I have an itty-bitty amount of basic knowledge about: Calculus, Linear Algebra, Discrete Mathematics, Probability & Statistics from university. Think about my interest in machine learning:

Can I learn and get a job in Machine Learning without studying CS Master and PhD?
- "You can, but it is far more difficult than when I got into the field." Drac Smith
How do I get a job in Machine Learning as a software programmer who self-studies Machine Learning, but never has a chance to use it at work?
- "I'm hiring machine learning experts for my team and your MOOC will not get you the job (there is better news below). In fact, many people with a master's in machine learning will not get the job because they (and most who have taken MOOCs) do not have a deep understanding that will help me solve my problems." Ross C. Taylor
What skills are needed for machine learning jobs?
- "First, you need to have a decent CS/Math background. ML is an advanced topic so most textbooks assume that you have that background. Second, machine learning is a very general topic with many sub-specialties requiring unique skills. You may want to browse the curriculum of an MS program in Machine Learning to see the course, curriculum and textbook." Uri
- "Probability, distributed computing, and Statistics." Hydrangea

I find myself in times of trouble.

AFAIK, There are two sides to machine learning:

Practical Machine Learning: This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill-defined questions. It’s the mess of reality.
Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

I think the best way for practice-focused methodology is something like 'practice — learning — practice', that means where students first come with some existing projects with problems and solutions (practice) to get familiar with traditional methods in the area and perhaps also with their methodology. After practicing with some elementary experiences, they can go into the books and study the underlying theory, which serves to guide their future advanced practice and will enhance their toolbox of solving practical problems. Studying theory also further improves their understanding on the elementary experiences, and will help them acquire advanced experiences more quickly.

It's a long plan. It's going to take me years. If you are familiar with a lot of this already it will take you a lot less time.

How to use it

Everything below is an outline, and you should tackle the items in order from top to bottom.

I'm using Github's special markdown flavor, including tasks lists to check progress.

Create a new branch so you can check items like this, just put an x in the brackets: [x]

More about Github-flavored markdown

Follow me

I'm a Vietnamese Software Engineer who is really passionate and wants to work in the USA.

How much did I work during this plan? Roughly 4 hours/night after a long, hard day at work.

I'm on the journey.

Twitter: @Nam Vu


USA as heck

Don't feel you aren't smart enough

I get discouraged from books and courses that tell me as soon as I open them that multivariate calculus, inferential statistics and linear algebra are prerequisites. I still don’t know how to get started…

About Video Resources

Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so, but sometimes the classes are no longer in session so you have to wait a couple of months, so you have no access. I'm going to be adding more videos from public sources and replacing the online course videos over time. I like using university lectures.

Prerequisite Knowledge

This short section were prerequisites/interesting info I wanted to learn before getting started on the daily plan.

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 notes, do the exercises and write an implementation in Python or R.

Motivation

Dream

Machine learning overview

Machine learning mastery

Machine learning is fun

Machine Learning is Fun!
Part 2: Using Machine Learning to generate Super Mario Maker levels
Part 3: Deep Learning and Convolutional Neural Networks
Part 4: Modern Face Recognition with Deep Learning
Part 5: Language Translation with Deep Learning and the Magic of Sequences
Part 6: How to do Speech Recognition with Deep Learning

Inky Machine Learning

Machine learning: an in-depth, non-technical guide

Stories and experiences

Machine Learning Algorithms

10 Machine Learning Algorithms Explained to an ‘Army Soldier’
Top 10 data mining algorithms in plain English
10 Machine Learning Terms Explained in Simple English
A Tour of Machine Learning Algorithms
The 10 Algorithms Machine Learning Engineers Need to Know
Comparing supervised learning algorithms
Machine Learning Algorithms: A collection of minimal and clean implementations of machine learning algorithms

Beginner Books

Practical Books

Kaggle knowledge competitions

Video Series

MOOC

Resources

Games

Becoming an Open Source Contributor

Podcasts

Podcasts for Beginners:
"More" advanced podcasts
Podcasts to think outside the box:
- Data Stories

Communities

Conferences

Neural Information Processing Systems (NIPS)
International Conference on Learning Representations (ICLR)
Association for the Advancement of Artificial Intelligence (AAAI)
IEEE Conference on Computational Intelligence and Games (CIG)
IEEE International Conference on Machine Learning and Applications (ICMLA)
International Conference on Machine Learning (ICML)

Interview Questions

My admired companies

ELSA - Your virtual pronunciation coach

License

studiovc/machine-learning-for-software-engineers

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