Top-down learning path: Machine Learning for Software Engineers

9 minute read


Machine Learning for Software Engineers Inspired by Google Interview University.

If you like this project, please give me a star.

What is it?

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

The 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

Why use it?

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

I find myself in times of trouble.

AFAIK, There are two sides to machine learning:

  • Practical Machine Learning: This is about queries 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 are really passionate and want 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.

Nam Vu - Top-down learning path: machine learning for software engineers
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 can 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 note, do the exercises and write an implementation in Python or R.


Machine learning overview

Machine learning mastery

Machine learning is fun

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

Stories and experiences

Beginner Books

Practical Books

Kaggle knowledge competitions

Video Series



Becoming an Open Source Contributor


My admired companies

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