FreeML

Machine Learning:

- Videos:

• Stanford Statistical Learning: Course page
• Coursera Stanford by Andrew Ng: Coursera, Youtube
• Stanford 229: Youtube, Course page
• Machine Learning Foundations (機器學習基石): Coursera , Youtube
• Machine Learning Techniques (機器學習技法): Youtube
• CMU 701 by Tom Mitchell: Course page

- Textbooks:

• Introduction to Statistical Learning: pdf
• Computer Age Statistical Inference: Algorithms, Evidence, and Data Science: pdf
• The Elements of Statistical Learning: pdf

Natural Language Processing:

- Videos:

• Stanford - Basic NLP course on Coursera: Videos, Slides
• Stanford - CS224n Natural Language Processing with Deep Learning: Course web, Videos
• CMU - Neural Nets for NLP 2017: Course web, Videos
• University of Oxford and DeepMind - Deep Learning for Natural Language Processing: 2016-2017: Course web, Videos and slides
• Sequence Models by Andrew Ng on Coursera: Coursera

- Books:

• https://web.stanford.edu/~jurafsky/slp3/
• https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf
• Some chapters or sections of www.deeplearningbook.org
• http://u.cs.biu.ac.il/~yogo/nnlp.pdf (Goldberg also published a new book this year)

- Packages:

• http://www.nltk.org/
• https://nlp.stanford.edu/software/

Deep Learning

- Videos:

• Ng’s deep learning courses: Coursera. This specialization is so popular but only the first three (4 by Oct 31) courses are open. Prof. Ng covers all a lot of details and he is really a good teacher.
• Tensorflow. Stanford CS20SI: Youtube
• Stanford 231n: Convolutional Neural Networks for Visual Recognition (Spring 2017): Youtube, Couse page
• Stanford 224n: Natural Language Processing with Deep Learning (Winter 2017): Youtube, Course page The self-driving car is a really hot topic recently. Take a look at this short course to see how it works. MIT 6.S094: Deep * Learning for Self-Driving Cars: Youtube, Couse page
• Neural Networks for Machine Learning by Hinton: Coursera. This course is so hard for me but it covers almost everything about neural networks. Prof. Hinton is the hero.

- Books:

• Deep learning book by Ian Goodfellow: http://www.deeplearningbook.org/. Very detailed reference book.
• ArXiv for research updates: https://arxiv.org/. I found it the mobile version of Feedly is useful to follow ArXiv. Also, try https://deeplearn.org/ or http://www.arxiv-sanity.com/top.

Reinforcement Learning:

- Videos:

• UCL Course on RL by David Silver: Course page
• CS 294: Deep Reinforcement Learning by UC Berkeley, Fall 2017: Course page

- Books:

• Reinforcement Learning: An Introduction (2nd): pdf

Others:

• Recommender System by UMN: Coursera
• Mining Massive Datasets by Stanford: Free book, Course
• Introduction to Algorithms by MIT: Course page with videos
• Database by Stanford: Course

Interviews:

- Lists with Solutions:

• 111 Data Science Interview Questions & Detailed Answers: Link
• 40 Interview Questions asked at Startups in Machine Learning / Data Science Link
• 100 Data Science Interview Questions and Answers (General) for 2017 Link
• 21 Must-Know Data Science Interview Questions and Answers Link
• 45 Questions to test a data scientist on basics of Deep Learning (along with solution) Link
• 30 Questions to test a data scientist on Natural Language Processing Link
• Questions on Stackoverflow: Link

- Without Solutions:

• Over 100 Data Science Interview Questions Link
• 20 questions to detect fake data scientists Link
• Question on Glassdoor: link