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Deep Dive Materials

These are my materials for Deep Dive sessions to ML using popular Python libraries.

These are my materials for Deep Dive sessions to ML using popular Python libraries, numpy, scipy, scikit-learn, matplotlib and pandas. I summarized this from a lot of other material scattered in the web.

https://github.com/krsnewwave/DeepDive


Bonus! A very original contribution though is extracted senators’ speeches from the 2010 PH Senatorial Elections. Check it out!

https://github.com/krsnewwave/DeepDive/blob/master/data/ExtractedSenatorSpeeches.zip


You may have noticed the weeks are a little jumbled. The gaps are either for non-lecture weeks or for Kaggle sessions. The ‘stream of learning’ is the following:

  1. Get comfy with Ipython notebooks and numpy on week 0. Produce some viz using matplotlib.
  2. Go regression and simple classification on week 1. Get started with feature engineering.
  3. Run through of advanced models in week 2.
  4. Reporting in week 3.
  5. Advice time! Tussle with regularization in week 4. Get fancy with feature transformations. Santander Kaggle homework.
  6. Natural language processing on week 5. Something really awesome we did was analyze PH Senator’s speeches. Data provided!
  7. Random Acts of Pizza and more Santander from Kaggle for week 6.
  8. Unsupervised learning on week 7. Preparation for Capstone Project.
  9. Consultation on the Capstone project.
  10. An ML Competition bootcamp happened. See the MNIST folder.
  11. Capstone presentations!

Acknowledgements to Andrew Ng’s work, NLTK contributors, and the other sources I’ve linked to in the material.

By krsnewwave

I'm a software engineer and a data science guy on recommender systems, natural language processing, and computer vision.

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