- 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
- Introduction to Statistical Learning: pdf
- Computer Age Statistical Inference: Algorithms, Evidence, and Data Science: pdf
- The Elements of Statistical Learning: pdf
- 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
- 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)
- 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.
- 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.
- LSTM: My collection
- Docker Mastery: Udemy
- The Ultimate Hands-On Hadoop: Udemy
- Spark and Python for Big Data with PySpark: Udemy
- Lean Analytics: Amazon
- Data Science for Business: Amazon
- Data Smart: Amazon
- Storytelling with Data: Amazon
- UCL Course on RL by David Silver: Course page
- CS 294: Deep Reinforcement Learning by UC Berkeley, Fall 2017: Course page
- Reinforcement Learning: An Introduction (2nd): pdf
- 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
- 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
- Compare two models: My collection
- Over 100 Data Science Interview Questions Link
- 20 questions to detect fake data scientists Link
- Question on Glassdoor: link
- Bayesian Statistics: From Concept to Data Analysis: Coursera
- Bayesian Methods for Machine Learning: Coursera
- Statistical Rethinking: Course Page (Recorded Lectures: Winter 2015, Fall 2017)
- Bayesian Data Analysis, Third Edition
- Applied Predictive Modeling
- Time Series Forecasting (Udacity): Udacity
- Topics in Mathematics with Applications in Finance (MIT): Course page, Youtube
- Time Series Analysis and Its Applications: Springer
- https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
- https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/
- More: https://machinelearningmastery.com/?s=Time+Series&submit=Search
- Heard on the Street: Quantitative Questions from Wall Street Job Interviews by Timothy Falcon Crack: Amazon
- A Practical Guide To Quantitative Finance Interviews by Xinfeng Zhou: Amazon
- Financial Markets with Robert Shiller (Yale): Youtube, Coursera
- Topics in Mathematics with Applications in Finance (MIT): Youtube, Course page
- A Collection of Dice Problems: pdf
- Computer Science courses with video lectures: https://github.com/Developer-Y/cs-video-courses
- The Open Source Data Science Masters: http://datasciencemasters.org