26 KiB
Top-down learning path: Machine Learning for Software Engineers
Inspired by Google Interview University.
Translations: Brazilian Portuguese
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?
- Sides of machine learning?
- 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
- 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
- Podcasts
- Communities
- Interview Questions
- My admired companies
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. 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.
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…
- What if I’m Not Good at Mathematics
- 5 Techniques To Understand Machine Learning Algorithms Without the Background in Mathematics
- How do I learn machine learning?
Prerequisite Knowledge
This short section were prerequisites/interesting info I wanted to learn before getting started on the daily plan.
- What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?
- Learning How to Learn
- Don’t Break The Chain
- How to 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
Math Fundamentals
- Coding the Matrix torrent
- Coursera introduction to statistics 101
- [Linear Algebra essentials (https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
Machine learning overview
- Visual Interactive Guide To Basics - Neural Networks
- A Visual Introduction to Machine Learning
- A Gentle Guide to Machine Learning
- Machine Learning basics for a newbie
- How do you explain Machine Learning and Data Mining to non Computer Science people?
- Machine Learning: Under the hood. Blog post explains the principles of machine learning in layman terms. Simple and clear
- What is machine learning, and how does it work?
Machine learning mastery
- The Machine Learning Mastery Method
- Machine Learning for Programmers
- Applied Machine Learning with Machine Learning Mastery
- Python Machine Learning Mini-Course
- Machine Learning Algorithms Mini-Course
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
Machine learning: an in-depth, non-technical 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 in a Week
- Machine Learning in a Year
- How I wrote my first Machine Learning program in 3 days
- Learning Path : Your mentor to become a machine learning expert
- You Too Can Become a Machine Learning Rock Star! No PhD
- How to become a Data Scientist in 6 months: A hacker’s approach to career planning
- 5 Skills You Need to Become a Machine Learning Engineer
- Are you a self-taught machine learning engineer? If yes, how did you do it & how long did it take you?
- How can one become a good machine learning engineer?
- A Learning Sabbatical focused on Machine Learning
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
Beginner Books
- Data Smart: Using Data Science to Transform Information into Insight 1st Edition
- Data Science for Business: What you need to know about data mining and data analytic-thinking
- Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
Practical Books
- Machine Learning for Hackers
- Python Machine Learning
- Programming Collective Intelligence: Building Smart Web 2.0 Applications
- Machine Learning: An Algorithmic Perspective, Second Edition
- Introduction to Machine Learning with Python: A Guide for Data Scientists
- Data Mining: Practical Machine Learning Tools and Techniques, Third Edition
- Teaching material
- Machine Learning in Action
- Reactive Machine Learning Systems(MEAP)
- An Introduction to Statistical Learning
- Building Machine Learning Systems with Python
- Learning scikit-learn: Machine Learning in Python
- Probabilistic Programming & Bayesian Methods for Hackers
- Probabilistic Graphical Models: Principles and Techniques
- Machine Learning: Hands-On for Developers and Technical Professionals
- Learning from Data
- Reinforcement Learning: An Introduction (2nd Edition)
Kaggle knowledge competitions
- Kaggle Competitions: How and where to begin?
- How a Beginner Used Small Projects To Get Started in Machine Learning and Compete on Kaggle
- Master Kaggle By Competing Consistently
Video Series
- Machine Learning for Hackers
- Fresh Machine Learning
- Machine Learning Recipes with Josh Gordon
- Everything You Need to know about Machine Learning in 30 Minutes or Less
- Nuts and Bolts of Applying Deep Learning - Andrew Ng
- BigML Webinar
- mathematicalmonk's Machine Learning tutorials
- Machine learning in Python with scikit-learn
- My playlist – Top YouTube Videos on Machine Learning, Neural Network & Deep Learning
- 16 New Must Watch Tutorials, Courses on Machine Learning
MOOC
- Practical Deep Learning For Coders, Part 1
- Udacity’s Intro to Machine Learning
- Udacity’s Supervised, Unsupervised & Reinforcement
- Machine Learning Foundations: A Case Study Approach
- Coursera’s Machine Learning
- Machine Learning Distilled
- BigML training
- Coursera’s Neural Networks for Machine Learning
- Taught by Geoffrey Hinton, a pioneer in the field of neural networks
- Machine Learning - CS - Oxford University
- Creative Applications of Deep Learning with TensorFlow
- Intro to Descriptive Statistics
- Intro to Inferential Statistics
Resources
- Machine Learning for Developers
- Machine Learning Advice for Developers
- Machine Learning For Complete Beginners
- Machine Learning Self-study Resources
- Level-Up Your Machine Learning
- Enough Machine Learning to Make Hacker News Readable Again
- Dive into Machine Learning
- Flipboard Topics
- Medium Topics
- Monthly top 10 articles
- Machine Learning
- Algorithms
- Comprehensive list of data science resources
- Machine Learning Summer Schools
- DigitalMind's Artificial Intelligence resources
- Awesome Machine Learning
- CreativeAi's Machine Learning
Becoming an Open Source Contributor
- tensorflow/magenta: Magenta: Music and Art Generation with Machine Intelligence
- tensorflow/tensorflow: Computation using data flow graphs for scalable machine learning
- cmusatyalab/openface: Face recognition with deep neural networks.
- tensorflow/models/syntaxnet: Neural Models of Syntax.
Podcasts
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Podcasts for Beginners:
-
"More" advanced podcasts
-
Podcasts to think outside the box:
Communities
-
Quora
-
Reddit
##Interview Questions
- How To Prepare For A Machine Learning Interview
- 40 Interview Questions asked at Startups in Machine Learning / Data Science
- 21 Must-Know Data Science Interview Questions and Answers
- Top 50 Machine learning Interview questions & Answers
- Machine Learning Engineer interview questions
- Popular Machine Learning Interview Questions
- What are some common Machine Learning interview questions?
- What are the best interview questions to evaluate a machine learning researcher?
- Collection of Machine Learning Interview Questions