2018 has been a very busy year. Along with the day job, this post records all the new learnings in the data science side. Below is a month wise summary:

February:

I became a father.

May:
  • (re)Started my Machine Learning Journey by starting Neural Networks. Thanks to Travis Oliphant for suggesting me to start learning neural networks.
June:
  • Neural Networks and Deep Learning [DeepLearning.ai] [Coursera]
July:
  • Improving Deep Neural Networks, Hyperparameter tuning, Regularization and Optimisation [Coursera]
August:
  • Data Scientist with Python Track [DataCamp.com]
  • Convolution Neural Networks [Coursera]
November:
  • GE Analytics Engineer Certification completion. Thanks to my manager at work B Chandra Kesavan for motivating me to complete this at the start of the year.
  • GE Hackathon [my first Machine Learning hackathon participation]
December:
  • Probability and Statistics [in-progress] [Stanford Lagunita]
  • Statistical Learning [planned] [Stanford Lagunita]

I came across many statistical terms and fundamentals whilst working on the Analytics Engineer certification and whilst participating in the hackathon. Although I learnt them from various sources whilst finishing the certificaiton case study, I wanted to do a graduate level course that covered all the fundamental systematically (thanks to Pranav Raina for pointing that). The December schedule are to cover those fundamentals that were missing from the previous courses - both the Stanford Lagunita courses - covers them in good depth, and I also get my hands dirty on R, because they don’t have a Python option!

Plan for 2019:

  1. Time series Analysis and Prediction

  2. Sequence Models - This course is very heavy with fundamentals and programming - I did start in December - but wanted to keep the month lighter and catch up on some missing things.

  3. Build systems around neural networks for application in Mechanical Engineering.

  4. Continue work on the signal processing application, Siglyser App - which means learning more Flask.

  5. Develop a small IoT gadget that does some remote communication or data collection.

  6. Learn Python Generators in detail.

  7. Develop a python library that will support the Siglyser App.

  8. Learn vim