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Merge pull request #285 from miwojc/addFastai 

Added Fast.ai Introduction to Machine Learning for Coders
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@ -684,6 +684,11 @@ Topics covered include probability theory and Bayesian inference; univariate dis
- [Book](http://stanford.edu/class/ee103/mma.html)
- [Assignments](http://stanford.edu/class/ee103/homework.html)
- [Code](http://stanford.edu/class/ee103/julia_files)
- [Fast.ai Introduction to Machine Learning for Coders](http://course.fast.ai/ml.html) *Fast.ai / University of San Francisco* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4dd.png" width="20" height="20" alt="Lecture Notes" title="Lecture Notes" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4bb.png" width="20" height="20" alt="Assignments" title="Assignments" /> <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4da.png" width="20" height="20" alt="Readings" title="Readings" />
- There are around 24 hours of lessons, and you should plan to spend around 8 hours a week for 12 weeks to complete the material. The course is based on lessons recorded at the University of San Francisco for the Masters of Science in Data Science program. We assume that you have at least one year of coding experience, and either remember what you learned in high school math, or are prepared to do some independent study to refresh your knowledge.
- [Lecture Videos](http://course.fast.ai/lessonsml1/lessonsml1.html)
- [Lecture Notes](https://medium.com/@hiromi_suenaga/machine-learning-1-lesson-1-84a1dc2b5236)
- [Jupyter Notebooks](https://github.com/fastai/fastai/tree/master/courses/ml1)
- [Info 290](http://www.ischool.berkeley.edu/courses/i290-abdt) **Analyzing Big Data with Twitter** *UC Berkeley school of information* <img src="https://assets-cdn.github.com/images/icons/emoji/unicode/1f4f9.png" width="20" height="20" alt="Lecture Videos" title="Lecture Videos" />
- In this course, UC Berkeley professors and Twitter engineers provide lectures on the most cutting-edge algorithms and software tools for data analytics as applied to Twitter's data. Topics include applied natural language processing algorithms such as sentiment analysis, large scale anomaly detection, real-time search, information diffusion and outbreak detection, trend detection in social streams, recommendation algorithms, and advanced frameworks for distributed computing.
- [Lecture Videos](http://www.ischool.berkeley.edu/newsandevents/audiovideo/webcast/21963)