1. Missing Value Treatment
  2. Data Scaling & Normalization
  3. Hypothesis Testing
  4. Parameter Optimization in Neural Networks
  5. Optimizer
  6. Search Strategies

Articles by other Authors

IIT Jodhpur MTech(Artificial Intelligence)

Semester 1 :

Semester 2 :

Classical Machine Learning

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. It is the science of getting computers to act without being explicitly programmed.

Here are a few materials:

  • Statistics Book - Business Statistics - A First Course by David M. Levine, David F. Stephan, Kathryn A. Szabat, P.K. Viswanatha

  • Introduction to Probability and Statistics( For Scientists and Engineers)

  • Head First Statistics by Dawn Griffiths - Available at Safari Books Online

  • YouTube Channel - StatQuest with Josh Starmer is a good place to start.

  • Introduction to Statistical Learning in R (ISLR) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

  • YouTube video series of ISLR

  • The Hundred-Page Machine Learning Book by Andriy Burkov. This book will provide a concise understanding of various ML Algorithms. It comes with a wiki with additional information like Q&A, code snippets, further reading, tools, and other relevant resources. Having a prior understanding of various ML Techniques is advised.

  • Python for Data Science. Taking some Udemy course will help.

Deep Learning

Deep Learning is a sub field of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

Here are a few materials: