Notes: Machine Learning

I have been an avid learner as far as statistics and machine learning is concerned. My primary reason for exploring them has been the use-cases cutting across different domains and problems.

I have had the opportunity to explore healthcare, trading and sports analytics in the form of cricket, where I have been able to both formulate and solve a bunch of interesting problems.

Machine Learning Basics

Machine Learning Essentials

Case Study: Optimisation (Optrees Package)

Case Study : Interpreting Regression (Basics + Assumptions)

In the process, I would often end of re-using similar problem-solving approaches and often wanted to remember all the cool tricks/hacks and hard-learned concepts I have had been able to work-out all these years. Some of these have allowed me to win some interesting competitions as well.

Auquan Financial Time Series

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Besides improving your odds of winning these, I decided to write some notes to help collate concepts and takeaways from ML in an intuitive fashion. If you are one of those guys who love diagrams and intuition over maths to better understand stuff, you would love them.

I might have made small mistakes in calculations or derivations, please be careful and point them out in the case, you identify them. I have updated the documents in case of one of the notes set.