Inspired by Google Interview University.
This is my multi-month study plan for going from backend developer 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.
- 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
I'm following this plan to prepare for my near-future job: Machine learning engineer.
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
I'm a 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.
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…
- 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?
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.
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
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.
- A Visual Introduction to Machine Learning
- A Gentle Guide to Machine Learning
- Introduction to Machine Learning for Developers
- 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?
- Deep Learning - A Non-Technical Introduction
- 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!
- 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
- 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
- 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
- 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
- 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
- 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)
- Machine Learning with TensorFlow(MEAP)
- 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
- 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
- A Friendly Introduction to Machine Learning
- 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
- DeepLearning.TV
- Learning To See
- Neural networks class - Université de Sherbrooke
- 21 Deep Learning Videos, Tutorials & Courses on Youtube from 2016
- 30 Top Videos, Tutorials & Courses on Machine Learning & Artificial Intelligence from 2016
- Practical Deep Learning For Coders
- 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
- 6.S094: Deep Learning for Self-Driving Cars
- 6.S191: Introduction to Deep Learning
- Learn Machine Learning in a Single Month
- The Non-Technical Guide to Machine Learning & Artificial Intelligence
- Best practices rule book for Machine Learning engineering from Google
- Machine Learning for Software Engineers on Hacker News
- Machine Learning for Developers
- Machine Learning Advice for Developers
- Machine Learning For Complete Beginners
- Getting Started with Machine Learning: For absolute beginners and fifth graders
- How to Learn Machine Learning: The Self-Starter Way
- Machine Learning Self-study Resources
- Level-Up Your Machine Learning
- An Honest Guide to Machine Learning
- Enough Machine Learning to Make Hacker News Readable Again
- Dive into Machine Learning
- {Machine, Deep} Learning for software engineers
- Deep Learning For Beginners
- Machine Learning courses in Universities
- Flipboard Topics
- Medium Topics
- Monthly top 10 articles
- Machine Learning
- Algorithms
- Comprehensive list of data science resources
- DigitalMind's Artificial Intelligence resources
- Awesome Machine Learning
- CreativeAi's Machine Learning
- Halite: A.I. Coding Game
- Vindinium: A.I. Programming Challenge
- General Video Game AI Competition
- Angry Birds AI Competition
- The AI Games
- Fighting Game AI Competition
- CodeCup
- Student StarCraft AI Tournament
- AIIDE StarCraft AI Competition
- CIG StarCraft AI Competition
- CodinGame - AI Bot Games
- 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.
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Quora
-
Reddit
- 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)
- 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
- 121 Essential Machine Learning Questions & Answers