Tutorials

Richard Feynman: “I learned very early the difference between knowing the name of something and knowing something.”

This page will serve as a near exhaustive list for all tutorials related to Data Science.

This link contains almost everything, that is covered in rest of the page 😊

• Dive into Machine Learning

From the resources mentioned below, please complete at least one from each section before moving to the next section.

If you are curious to learn and earn a certificate as well, then use the financial aids on coursera. And as a pay bask please [translate] their course contents. The application for aid is approved in at most 15 days.

Edx provides you with 90% of scholarship as well! ( but do not exploit these financial aids! )

• Basic

  • Basic python for Data science
  • Data science handbook
  • Notes of Andrew Ng's course on Machine Learning(ML)

• Intermediate

  • basic statistics , it is recommended to have a overview of certain concepts in this course, so that you are not intimidated by the terminologies used in data-science and machine learning!
  • Fundamentals of Machine Learning
  • Data science handbook
  • kaggle tutorial , a word of caution! many don't find it easy to follow.

• Advanced (for these topics please find yourself a study group, reddit and the discussion of these courses will come in handy)

  • Probabilistic Graphical Model
  • Distributed computing using Spark

• Deep Learning

  • Deep learning by Andrew Ng
  • Deep learning by IBM
  • udacity's intro to deeplearning using pytorch
  • natural language processing course by Stanford university (level:Advanced)
  • Computer vision course by Stanford university (level: Advanced)
  • Self driving cars course by MIT (level: Advanced-research)

• Frameworks (please master at least one of the following, hack their tutorials and try to improve the model)

  • TF
  • Pytorch

• Miscellaneous resources