Resources I used for the path of data science.

You are always welcome to drop me an email at: xiongxinland@gmail.com

 * Mark-RCNN - * code link 1

• Learn Python the hard way: Free book
• Stanford Statistical Learning (Course page) or Coursera Stanford by Andrew Ng (Coursera, Youtube)
• Ng’s deep learning courses: Coursera
• Stanford - Basic NLP course on Coursera: Videos, Slides
• Keras in 30 sec: Link
• Deeplearning keywords : link

• Stanford Statistical Learning: Course page
• Coursera Stanford by Andrew Ng: Coursera, Youtube
• Stanford 229: Youtube, Course page
• Machine Learning Foundations : Coursera , Youtube
• Machine Learning Techniques : 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

• Speech and Language Processing (3rd ed. draft): Book
• An Introduction to Information Retrieval: pdf
• Deep Learning (Some chapters or sections): Book
• A Primer on Neural Network Models for Natural Language Processing: Paper. Goldberg also published a new book this year

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

Some other people's collections: NLP, DL-NLP, Speech and NLP, Speech, RNN

• Ng’s deep learning courses: Coursera. This specialization is so popular. 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.
• FAST.ai: Course

• 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.

• Lean Analytics: Amazon
• Data Science for Business: Amazon
• Data Smart: Amazon
• Storytelling with Data: Amazon

• Docker Mastery: Udemy
• The Ultimate Hands-On Hadoop: Udemy
• Spark and Python for Big Data with PySpark: Udemy

• Udacity: Course
• 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
• How to Win a Data Science Competition: Coursera
• How to finish a Data Challenge: Kaggle EDA kernels

• 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

• Udacity software engineering: 1, 2, 3 -Ongoing-
• Stanford 224n
• Topics in Mathematics with Applications in Finance (MIT): Youtube, Course
• FAST.ai part2: http://course.fast.ai/part2.html
• CS 294: Deep Reinforcement Learning: http://rll.berkeley.edu/deeprlcourse/
• CMU 701 by Tom Mitchell: http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml
• Cryptography: https://www.coursera.org/learn/crypto 
• Statistical Rethinking: http://xcelab.net/rm/statistical-rethinking/
• Probabilistic Graphical Models: https://www.coursera.org/specializations/probabilistic-graphical-models
• Bitcoin and Cryptocurrency Technologies：https://www.coursera.org/learn/cryptocurrency
• Compiler：https://lagunita.stanford.edu/courses/Engineering/Compilers/Fall2014/about
• NTU - Machine Learning (2017,Fall) http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML17_2.html