Resources I used for the path of data science.
You are always welcome to drop me an email at: [email protected]
* 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
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