R, Python, Machine Learning and AI
- CS109 Data Science: Such an excellent course! It is a general yet quite advanced course that introduced to students a lot of topics in data science including web scraping, statistical inference, machine learning (regression, regularization, classification, SVM, decision trees, ensemble methods), Bayesian statistics, big data (MapReduce, Spark) and visualization (Tableau). Homeworks from previous years are available for practice.
- The Analytics Edge on Edx: Strongly recommended if anyone wants to learn R programming! Filled with case studies and tons of exercises for practice.
- Data Science Specialization on Coursera: Not recommended for starters. A good course introducing all the concepts in data science, yet not enough exercises and some of the courses are difficult to follow without some background. There is this excellent R markdown course note for this specialization by Xing everyone should check out if planning to start this specialization.
- Introduction to Statistical Learning by Trevor Hastie and Rob Tibshirani
- Machine learning by Andrew Ng on Cousera: One of the best courses ever! He explains machine learning concepts in such an elegant way, and easy to understand. It would be better if this course is taught in Python though. I know Octave is excellent in matrix manipulation and more research oriented, but I’m not sure if I will be using it ever.
- Machine Learning Specialization on Coursera: Great course! It is taught by two amazon professors. In the first course of this specialization, they explain all the concepts (regression, classification, clustering and recommendation system) in a light way. There’s not much hard core algorithm yet, and it will be coming up in the following courses according to their agenda.
- Plan to read:
- Hadley Wickham – R for Data Science, Advanced R, R packages
- CPSC 340 Machine Learning and Data Mining by Nando de Freitas UBC
- CPSC 540 Machine Learning by Nando de Freitas UBC
- Recorded tutorial videos from major python conferences such as Scipy, Pycon, Pydata (strongly recommended). Check out my Youtube playlists, especially:
- Introduction to NumPy | SciPy 2015 Tutorial | Eric Jones
- Brandon Rhodes – Pandas From The Ground Up – PyCon 2015
- Analyzing and Manipulating Data with Pandas | SciPy 2015 Tutorial | Jonathan Rocher
- Anatomy of matplotlib | SciPy 2015 Tutorial | Benjamin Root and Joe Kington
- Jake VanderPlas – Machine Learning with Scikit-Learn (I) – PyCon 2015
- Olivier Grisel – Machine Learning with Scikit-Learn (II) – PyCon 2015
- Computational Statistics I | SciPy 2015 Tutorial | Allen Downey
- Computational Statistics II | SciPy 2015 Tutorial | Chris Fonnesbeck
- Allen Downey – Bayesian statistics made simple – PyCon 2015
- Bayesian Statistical Analysis using Python – Part 1 | SciPy 2014 | Chris Fonnesbeck
- Introduction to Natural Language Processing by Dragomir R. Radev on Coursera
- Neural Networks for Machine Learning by Geoffrey Hinton on Coursera
- Stanford CS231n: Convolutional Neural Networks for Visual Recognition by Andrej Karpathy
- UCL Course on Reinforcement Learning by David Silver
- Deep Learning for Self-Driving Cars
- Practical Deep Learning For Coders by Jeremy Howard (Kaggle’s #1 competitor 2 years running, and founder of Enlitic)
- Probabilistic Graphical Models by Daphne Koller on Coursera
- Mining Massive Datasets on Coursera