Hello, everyone.

Following are first 2 weeks of this series. You can find them on my github repository. You can run all the notebook on colab or jupyter as well.

• Day 1 - Python Basics Part1

  Topic : Data Types, Strings, Operators, Chaining Comparison Operators with Logical Operators

• Day 2 - Python Basics Part2

  Topic : Python Lists and Dictionaries, Sets, Tuples and etc

• Day 3 - Python Basics Part3

  Topic : Loop, Break and Continue Statement, Object-Oriented Programming and Class

• Day 4 - Intermediate Python Part1

  Topic : First Class Function, Private variables, Global and Non Local variables, Magic Function, Tuple Unpacking, Static Variables and Method

• Day 5 - Intermediate Python Part2

  Topic : Lambda function, Matic methods, Inheritance and Polymorphism, Erros and Exception Handling, User-defined function, Python garbage collection, and debugger

• Day 6 - Advanced Python

  Topic : Decorators, Memoization using Decorators, Generators, Ordered and Defaultdict, Coroutine

• Day 7 - Statistics for Data Science and Machine Learning

  Topic : Statistics for Data Science

• Day 8 - Maths for Data Science and Machine Learning

  Topic : Linear Algebra, Calculus, Matrix and Vectors, Bayes Theorem and Cheatsheets

• Day 9 - Pandas Part1

  Topic : Pandas Series, DataFrame

• Day 10 - Pandas Part2

  Topic : Indexing, Filtering, Transformation, Merging, Hierarchial Indexing

• Day 11 - Numpy

  Topic : Flattening, Concatenation and Broadcasting

• Day 12 - Data PreProcessing Part1

  Topic : Encoding categorical data, Split data, Feature Scaling

• Day 13 - Data PreProcessing Part2

  Topic : Data Cleaning, Data Augmentation, Transformatoin, Channel Shift

• Day 14 - Regression Part1

  Topic : Simple Linear Regression, Multi Linear Regression, Polynomial Regression

Hope my work would be little bit helpful for AI enthusiastic.
If that, please star that repository then follow me on Github and Dev.to

https://dev.to/thunderstroke
https://github.com/thunderstroke325
Best Regards.