Study Group

The SML 109 Study Group is now complete. Over 13 weeks in 2017, we worked through Harvard’s CS109 Data Science Course as a group and finally presented 10 data science projects at an event titled “Judgement Day” at Amazon, a couple of videos from the event which are available on Youtube.

Feel free to check this page for updates on future Study Group Sessions!

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SML CS109 Student Discoveries Archive

The best student & TA discoveries related to the CS109 course:

Week 11 Oct 11th:

Michael’s discovery of essential cheat sheets for machine learning engineers

https://startupsventurecapital.com/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

Week 9 Sep 27th:

Best picks from the discussion on DS/ML/AI primer material:

Really Good: AI primer playbook

http://aiplaybook.a16z.com/docs/intro/getting-started

Data science for beginners series on youtube:

http://bit.ly/2jKUvZk

Tensorflow playground (this is awesome!)

http://playground.tensorflow.org/

 

Week 8 Sep 20th:

Khalido’s excellent cs109 course notes as he has worked through the material:

https://github.com/khalido/cs109-2015

Python Podcast, In particular this episode goes into Sklearn:

http://pca.st/dDy4

 

Week 7 Sep 13th:

extra data science courses to go a bit more in depth:

https://www.dataquest.io/

best python pandas resources:

http://www.dataschool.io/best-python-pandas-resources/

Week 6 Sep 6th:

Excellent presentations by our tutors Nikzad & Gordon explaining SVD & PCA:

SVD and PCA

PCA

What are kernels in machine learning and SVM and why do we need them:

https://www.quora.com/What-are-kernels-in-machine-learning-and-SVM-and-why-do-we-need-them

Week 5 Aug 30:

What is a T test?:

 

Bootstrap definition:

 

Simplified Explanation of Linear Regression:

 

“The Curse of Dimensionality”:

 

Intuitive Explanation between the relationship between PCA & SVD

https://www.quora.com/What-is-an-intuitive-explanation-of-the-relation-between-PCA-and-SVD

 

Week 4 Aug 23:

Python/Pandas/Numpy/Matplotlib Material:

Python Data Science Handbook:

https://jakevdp.github.io/PythonDataScienceHandbook/index.html

Extra Data Visualisation Tools:

gap-minder type plots (visualising data changing over time) with plotly:

https://plot.ly/python/animations/#using-a-slider-and-buttons

Extra Statistics Material:

Bootstrapping Juypter notebook explanation by Dima Galat:

https://github.com/PaulConyngham/bootstrappingexample/blob/master/Bootstrapping.ipynb

Khan Academy Stats Course:

https://www.khanacademy.org/math/statistics-probability

Box & Whisker pots :

http://www.flinders.edu.au/slc_files/Documents/Red%20Guides/Box%20and%20Whisker%20Plots.pdf

Bias, Variance & Bias-Variance trade off:

P-Value explanation:

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