Top-down learning path: Machine Learning for Software Engineers
Inspired by Coding Interview University.
Translations: Brazilian Portuguese | 中文版本 | Français | 臺灣華語版本
How I (Nam Vu) plan to become a machine learning engineer
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
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:
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.
 |
| 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 consists of 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
Machine learning overview
Machine learning mastery
Machine learning is fun
Machine Learning: An In-Depth Guide
- Overview, goals, learning types, and algorithms
- Data selection, preparation, and modeling
- Model evaluation, validation, complexity, and improvement
- Model performance and error analysis
- Unsupervised learning, related fields, and machine learning in practice
Stories and experiences
Machine Learning Algorithms
Beginner Books
Practical Books
Kaggle knowledge competitions
Video Series
MOOC
Resources
Games
Becoming an Open Source Contributor
Podcasts
Podcasts for Beginners:
Interviews with ML Practitioners, Researchers and Kagglers about their Joureny
"More" advanced podcasts
Podcasts to think outside the box:
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)
- International Joint Conferences on Artificial Intelligence (IJCAI)
- Association for Computational Linguistics (ACL)
Interview Questions
My admired companies
这是一套免费的自学课程路线,历时数月,帮助一名在职软件工程师从移动开发转型为机器学习工程师。它以 GitHub 清单的形式呈现,包含文章、书籍、课程和竞赛。