Resources
A list of useful resources (books, courses) I have read or taken.
Books
I have read a series books. Below are some selected books I have finished.
1. Machine Learning, Deep Learning, and Data Science
- The Elements of Statistical Learning (ESL) (link)
- By Trevor Hastie, Robert Tibshirani, Jerome Friedman
- Good reference book, need some mathematical background
- Introduction to Data Mining
- By Pang-Ning Tan, Michael Steinbach, Vipin Kumar
- Data Mining: Concepts and Techniques
- By Jiawei Han, Micheline Kamber, Jian Pei
- Outlier Analysis
- By Charu C. Aggarwal
- Practical Machine Learning: A New Look at Anomaly Detection
- By Ted Dunning, Ellen Friedman
- Deep Learning (link)
- By Ian Goodfellow, Yoshua Bengio, Aaron Courville
- Maybe a good reference book for researchers. Personally, I don’t like it
- Hands-On Machine Learning with Scikit-Learn & TensorFlow
- By Aurélien Géron
- Very practical book for machine learning with TensorFlow Strongly suggested
- Deep Learning with Python
- By François Chollet
- Very practical book for deep learning with Keras. Strongly suggested
- Data Science from Scratch
- By Joel Grus
- Practical Statistics for Data Scientists: 50 Essential Concepts
- By Peter Bruce, Andrew Bruce
2. Computer Science and Big Data
- Hadoop: The Definitive Guide
- By Tom White
- A lot of useful details about Hadoop
- Introduction to Programming Using Java
- By David J. Eck
- Head First Java
- By Kathy Sierra, Bert Bates
- Interesting book.
3. Other
- Cracking the Coding Interview
- By Gayle L. McDowell
- Useful tips for preparing coding interview.
- A Practical Guide to Quantitative Finance
- By Xinfeng Zhou
- A lot of interesting and useful questions
- Blockchain Basics: A Non-Technical Introduction in 25 Steps
- By Daniel Drescher
- A intro-book for Blockchain
- SQL in 10 Minutes
- By Ben Forta
Online Resources
1. Machine Learning and Data Science
- deeplearning.ai
- Deep Learning Specialization
- Contains 5 different topics. One of the best deep learning course ever.
- Stanford University
- Machine Learning
- Statistical Learning
- University of Toronto
- University of Washington
- Machine Learning
- Foundations, Regression, Classification, Clustering & Retrieval
2. Computer Science and Programming
- Stanford University
- Algorithms: Design and Analysis, Part 1 and Part 2
- Very useful course
- Princeton University
- Algorithms: Part 1
- Very useful course
- Swiss Federal Institute of Technology in Lausanne
- Functional Programming Principles in Scala
- I think they are boring lectures.
3. Other
- IBM
- IBM Blockchain Founcation for Developers
- Princeton University
- Bitcoin and Cryptocurrency Technologies