Sandeep Pandey

Sandeep Pandey

Engineer with a PhD in applied machine learning and scientific computing.

Resources: Data Science, Machine Learning, Python

2 minute read

Published:

This is a (growing) general-purpose list (for my own documentation purpose) of some of the resources which I have found very helpful.

Machine Learning (ML) and Data Science (DS)

Practial courses for ML:

In my opinion, before jumping to the ML or DS theory part (or even finalizing the journey), one should learn about why ML/DS is critical and how it looks. In the end, it’s just the cocktail of programming, applied mathematics, and domain knowledge. Following is the recommended list to begin with:

  • An excellent overview of deep learning from Lex Fridman on YouTube.
  • TensorFlow 2.0 Beginner Tutorials : Well described tutorials focusing upon TensorFlow with detailed concept from the application perspective.
  • Udemy is a go-to place to learn the application part. Courses can be bought often for a cheaper price and the good part is that they are structured and everything is in one place. I really enjoyed this course as a first step. This course can be bought for < 19 Euro (If it’s not in this price range then wait for some time, they often have a special offer like every other week).

Theoretical resources:

Python

Learning Python (basic and advance)

Swift

Applied Statistics

Markov Chain

Hidden Markov Model

Lyapunov Exponent

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