A complete daily plan for studying to become a machine learning engineer.
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README.md

Top-down learning path and resources: Machine Learning for Software Engineers

Inspired by Machine Learning for Software Engineers by Google Interview University.

Translations: Brazilian Portuguese

Table of Contents


Sides of Machine Learning:

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. Its 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.

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 dont know how to get started…

Prerequisite Knowledge

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

Math Fundamentals

Machine learning overview

Machine learning mastery

Machine learning is fun

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

Machine Learning in-depth math theory

Stories and experiences

Machine Learning Algorithms

Deep Learning Resources

Deep Learning To Read

Beginner Books

Practical Books

Kaggle knowledge competitions

Video Series

MOOC

Resources

Becoming an Open Source Contributor

Podcasts

Communities