This repository contains a comprehensive collection of Jupyter Notebooks covering Python fundamentals, core programming concepts, and essential libraries for Data Science as part of an AI & ML learning path.
The notebooks are organized by topic:
- Variables & Keywords:
1.Variables & Keywords.ipynb - Datatypes:
Datatypes.ipynb - Arithmetic Operations:
2.2Arithmetic Operations.ipynb - String Operations:
2.1String Operations.ipynb
- Control Structures:
Control Structures.ipynb - Loops & Iteration:
Loops & Iteration.ipynb - Functions:
Functions.ipynb - Exception Handling:
Exception Handling.ipynb
- Lists:
Lists.ipynb - Tuples:
Tuples.ipynb - Dictionaries:
Dictionary.ipynb - Sets:
Sets.ipynb
- Object-Oriented Programming (OOP):
OOPs in Python.ipynb - File Handling:
File Handling.ipynb - Iterators & Generators:
Iterators & Generators.ipynb - Map, Reduce & Filter:
map,reduce & filter.ipynb
- NumPy (Numerical Computing):
NumPy.ipynb - Pandas (Data Manipulation):
Pandas.ipynb - Matplotlib (Visualization):
Matplotlib.ipynb,Matplotlib-TL.ipynb - Seaborn (Advanced Visualization):
Seaborn.ipynb,Seaborn-TL.ipynb
- Normal Distribution & CLT:
Normal_Distribution_+_CLT.ipynb
Churn_Modelling.csv: Used for data analysis examples.
To explore these notebooks:
- Clone the repository:
git clone <repository-url>
- Install dependencies (recommended using Anaconda or pip):
pip install notebook numpy pandas matplotlib seaborn
- Launch Jupyter Notebook:
jupyter notebook
- Basic understanding of programming logic.
- Python installed (likely via Anaconda Distribution for Data Science).
These notebooks and materials are based on a Data Science course by Satyajit Pattnaik. They serve as my personal study notes and practice exercises from the curriculum taught by the instructor.