Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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The Best Machine Learning Resources! How to learn machine learning?

By Nand Kishor |Email | Mar 16, 2018 | 15564 Views

A compendium of resources for crafting a curriculum on artificial intelligence, machine learning, and deep learning.

This article is an addendum to the series Machine Learning for Humans, a guide for getting up-to-speed on machine learning concepts in 2-3 hours.

General advice on crafting a curriculum

Going to school for a formal degree program for isn't always possible or desirable. For those considering an autodidactic alternative, this is for you.

1. Build foundations, and then specialize in areas of interest.
You can't go deeply into every machine learning topic. There's too much to learn, and the field is advancing rapidly. Master foundational concepts and then focus on projects in a specific domain of interest‚??-‚??whether it's natural language understanding, computer vision, deep reinforcement learning, robotics, or whatever else.

2. Design your curriculum around topics that personally excite you.
Motivation is far more important than micro-optimizing a learning strategy for some long-term academic or career goal. If you're having fun, you'll make fast progress. If you're trying to force yourself forward, you'll slow down.

We've included resources that we explored personally or came highly recommended. This list is not meant to be exhaustive. There are endless options, and too much choice is counterproductive. But if we're missing a great resource that belongs here, please reach out!

Foundations
Programming
Syntax and basic concepts: Google's Python Class, Learn Python the Hard Way.
Practice: Coderbyte, Codewars, HackerRank.

Linear algebra
Deep Learning Book, Chapter 2: Linear Algebra. A quick review of the linear algebra concepts relevant to machine learning.

A First Course in Linear Model Theory by Nalini Ravishanker and Dipak Dey. Textbook introducing linear algebra in a statistical context.

Probability & statistics
MIT 18.05, Introduction to Probability and Statistics, taught by Jeremy Orloff and Jonathan Bloom. Provides intuition for probabilistic reasoning & statistical inference, which is invaluable for understanding how machines think, plan, and make decisions.
All of Statistics: A Concise Course in Statistical Inference, by Larry Wasserman. Introductory text on statistics.

Calculus
Khan Academy: Differential Calculus. Or, any introductory calculus course or textbook.
Stanford CS231n: Derivatives, Backpropagation, and Vectorization, prepared by Justin Johnson.

Machine learning
Courses
Andrew Ng's Machine Learning course on Coursera (or, for more rigor, Stanford CS229).

Data science bootcamps: Galvanize (full-time, 3 months, $$$$), Thinkful (flexible schedule, 6 months, $$).

Textbook
An Introduction to Statistical Learning by Gareth James et al. Excellent reference for essential machine learning concepts, available free online.

Deep learning
Courses
Deeplearning.ai, Andrew Ng's introductory deep learning course.
CS231n: Convolutional Neural Networks for Visual Recognition, Stanford's deep learning course. Helpful for building foundations, with engaging lectures and illustrative problem sets.

Projects
Fast.ai, a fun and hands-on project-based course. Projects include classifying images of dogs vs. cats and generating Nietzschean writing.
MNIST handwritten digit classification with TensorFlow. Classify handwritten digits with >99% accuracy in 3 hours with this tutorial by Google.
Try your hand at a Kaggle competition. Implement a deep learning paper that you found interesting, using other versions on GitHub as reference material.

Reading
Deep Learning Book, a.k.a. the Bible of Deep Learning, authored by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
Neural Networks and Deep Learning, a clear and accessible online deep learning text by Michael Nielsen. Ends with commentary on reaching human-level intelligence.
Deep Learning Papers Reading Roadmap, a compilation of key papers organized by chronology and research area.

Reinforcement learning
Courses
John Schulman's CS 294: Deep Reinforcement Learning at Berkeley.
David Silver's Reinforcement Learning course at University College London.
Deep RL Bootcamp, organized by OpenAI and UC Berkeley. Applications are currently closed, but it's worth keeping an eye out for future sessions.

Projects
Andrej Karpathy's Pong from Pixels. Implement a Pong-playing agent from scratch in 130 lines of code. 

Arthur Juliani's Simple Reinforcement Learning with Tensorflow series. Implement Q-learning, policy-learning, actor-critic methods, and strategies for exploration using TensorFlow. 

See OpenAI's requests for research for more project ideas.

Reading
Richard Sutton's book, Reinforcement Learning: An Introduction.

Artificial intelligence
Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig.  
Sebastian Thrun's Udacity course, Intro to Artificial Intelligence.
Fellowships: Insight AI Fellows Program, Google Brain Residency Program

Artificial intelligence safety
For the short version, read: (1) Johannes Heidecke's Risks of Artificial Intelligence, (2) OpenAI and Google Brain's collaboration on Concrete Problems in AI Safety, and (3) Wait But Why's article on the AI Revolution.
For the longer version, see Nick Bostrom's Superintelligence.

Check out the research published by the Machine Intelligence Research Institute (MIRI) and Future of Humanity Institute (FHI) on AI safety.

Keep up-to-date with /r/ControlProblem on Reddit.

Source: Medium