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Day to Day discussed external links for future reference

Day0 Pre-Training Resources

  1. Downloading and Installing Jupyter on Windows
  2. Markdown Syntax
  3. Github Cheat Sheet
  4. Official Python Website
  5. Python Tutorial

Data Sources

  1. Kaggle
  2. Google Datset Search Engine
  3. Data.World
  4. UC Irvine Machine Learning Repository

References

  1. NumPy
  2. Pandas
  3. Matplotlib
  4. Seaborn
  5. Scikit Learn

Data Analysis Practise

  1. 100 numpy exercises.
  2. Pandas Exercises
  3. Data Analysis Projects.
  4. matplotlib Cheat Sheets
  5. matplotlib-tutorial

Reference Courses

  1. Free Courses During Pandemic
  2. Git Version Control
  3. ML Course by Andrew Ng.
  4. ML Course from Free Code Camp
  5. Open Deep Learning and Reinforcement Learning lectures from top Universities
  6. Math Courses
    1. Intro to statistics
    2. Linear Algebra Refresher Course
    3. Intro to Descriptive Statistics
    4. Intro to Inferential Statistics
    5. Statistics & Probability
    6. Linear Algebra
    7. Calculus

Few more imp. suggestions

  1. Everyone should have professional LinkedIn profile for future job purpose build nice LinkedIn profile.
  2. If you are a programmer you should have GitHub profile with your learnings and projects.
  3. At the end of the day it is not only about learning but we have to apply the things which we have learned. so try to explore more on the concept and also share your knowledge to the community by writing a blog of what you have learned on day to day basis or on entire training program @Medium, or @dev.to, or write an article @LinkedIn.