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Posted by Kunal Jain

Here are my recommendations from these tutorials and the reasons why I like them:

  1. Exploring Machine learning with Scikit-learn and Diving deeper into Machine Learning with Scikit-learn - Just the right place to start learning about machine learning in Python. The 2 tutorials combined provide you with all the knowledge you need on the subject. The second tutorial covers some of the advanced topics like automated parameter tuning and how to scale up using iPython.parallel.
  2. Mining Social Web APIs with IPython Notebook - Coming from the author of popular book, mining the Social Web (O’Reilly, 2013), the tutorial and the accompanying iPython notebook provide you ways to extract data using social media APIs. Aimed towards beginners, the tutorial shows step by step extraction and mining of data. Once you know how to extract this data, you can get as creative as you want – How different are you from likes of your Facebook Friends? How much influence do you carry on Twitter? are just some of the questions to start your journey.
  3. Data Wrangling for Kaggle Data Science Competitions — An etude - A must read if you have participated in Kaggle competitions or want to at some point in near future. Krishna’s insights into how the competitions work and what data providers might do before providing data can save you months of scrolling through Kaggle Forums!
  4. Hands-on with Pydata: how to build a minimal recommendation engine - If you have not built a recommendation engine till now and are fascinated by the idea of building one, this is just what you needed. A tutorial which covers all the basics and quickly moves towards practical applications and the idea of iterational improvement.
I am left with another one Bayesian statistics made simple to watch – I am hoping that it is a good one as well. All these tutorials are awesome resources for learning Python for data analysis from some of the best minds across the globe.

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