Table of contents
Child pages
Career advice
- 2017.03.01 - Fast.ai - How to change careers and become a data scientist - one quant's experience
- 2017.04.06 - Fast.ai - Alternatives to a Degree to Prove Yourself in Deep Learning
Challenge sites
Reinforcement learning
Websites
Books
- Amazon - Reinforcement Learning: An Introduction
- Highly rec'd book
Non-deep-learning classifiers
Naive Bayes
Related pages
Tutorials
- 2017.05.25 - MonkeyLearn.com - A practical explanation of a Naive Bayes classifier
- 2017.09.11 - Analytics Vidhya - 6 Easy Steps to Learn Naive Bayes Algorithm (with codes in Python and R)
- In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.
Major websites
Minor websites
- Otoro - Machine Learning
- Ferenc Huszár
- DataSchool.io - In-depth introduction to machine learning in 15 hours of expert videos
Organizations
Companies
Learning resources
Articles
- 2017.07.28 - Mashable - This high school kid taught himself to be an AI wizard
incredibly, Mikel's programming skills in machine learning and AI are almost completely self-taught — there are no courses on building AI-powered systems at his high school in Guildford, just outside of London. He spent big chunks of the last couple of years researching AI and machine learning on the internet, reading articles and watching videos.
"There are lots of free courses online, but I actually didn't take courses. When I had a big problem I wanted to solve, I would just Google around and try to find out about it, so I didn't follow any predefined track. I read about Kaggle online, and I thought, 'Why not try it?'"
After entering various competitions over several months and slowly placing higher and higher, Mikel helped create an algorithm for using computer vision to analyze 8 million YouTube videos to create accurate tags. His team ended up 7th out of 650 teams.
- 2017.10.24 - Rainforest QA - Kaggle Perks in Real Data Science Work
- He rec's the following articles / forum posts:
- For non-trivial validation:
- Validation for pairs of items with a time component
- Time-series validation when you need to predict for some specific time in the future
- TODO: Grab and organize the rest of the links in this post.
- https://www.kaggle.com/c/planet-understanding-the-amazon-from-space/discussion/36809
- For non-trivial validation:
- He rec's the following articles / forum posts:
Bootcamps
- Zipfian Academy
- This is the one Justin went to.
Topic-specific
Markov Chains
- 2015.10.05 - Nicole White - Improving My CLI’s Autocomplete with Markov Chains
- This is very easy-to-follow; it would be easy to use the code here as a template for another project.
Computer Vision
- Tutorials
- www.pyimagesearch.com
- This guy seems really smart, I'd really like to work with him.
- www.pyimagesearch.com
- Talks
Books
- Erik Mueller (he was on the IBM Watson team)
- The Master Algorithm
- Apparently a good overview of all of the ML stuff out there.
- Programming Collective Intelligence
- Rec'd by someone who commented on another person's review for 'The Master Algorithm'.
Courses
Papers
- A Model Explanation System
- rec'd by Bob
Challenge sites
Libraries
- http://scikit-learn.org/
- http://scikit-image.org/
- simple-amt
- This is a wrapper for Amazon Mechanical Turk, useful for labelling data
Genetic programming
Books
- A Field Guide to Genetic Programming
- Introduction to Evolutionary Computing
- Genetic Programming by John Koza