This repository is a curated collection of Google Colab Notebooks and resources created by Lovnish Verma to learn and teach Python programming and Data Science concepts interactively. It covers foundational Python, Object-Oriented Programming, libraries like NumPy, Pandas, Matplotlib, Seaborn, Exception Handling, and real-world machine learning problems like the Titanic dataset.
- 🔰 Python basics to advanced topics
- 📊 Data visualization with Matplotlib and Seaborn
- 🧮 Scientific computing with NumPy
- 🐼 Data manipulation using Pandas
- 🧠 Machine learning using Scikit-Learn
- 🚢 Real-life datasets (Titanic, Iris)
- ✅ Concepts with clear examples and explanations
- 📘 Includes handwritten notes and markdown guides
| Notebook/File | Description |
|---|---|
🐍_Python_Getting_Started.ipynb |
Getting started with Python: syntax, data types, control structures |
python_basics.ipynb |
Covers recursion, factorial, Fibonacci, and file handling |
NumPY.ipynb |
Introduction to NumPy arrays, indexing, and vectorized operations |
🐼_Python_Pandas.ipynb |
Data manipulation using Pandas: Series, DataFrames, missing values |
Matplotlib_Visualization_with_Python.ipynb |
Core Matplotlib visualizations and plot customizations |
Matplotlib_Seaborn.ipynb |
Seaborn for advanced statistical plots and data styling |
Pandas.ipynb |
Additional Pandas operations and advanced data analysis |
Modules_and_Libraries_in_Python.ipynb |
Importing and using Python standard and external libraries |
Exception_Handling_in_Python.ipynb |
Try-except blocks, raising exceptions, and custom error handling |
Object_Oriented_Programming_(OOP).ipynb |
Concepts like classes, objects, inheritance, and polymorphism |
Oop_Python_Notebook.ipynb |
Practice notebook for OOP concepts |
Scikit_Learn_Machine_Learning_in_Python_.ipynb |
Introduction to Scikit-Learn for machine learning tasks |
TITANIC.ipynb |
Machine learning project on Titanic survival prediction |
iris(step_bystep).ipynb |
Step-by-step ML classification on the Iris dataset |
BDDS_17march_01.ipynb |
Lecture notebook on data science topics covered in class |
guide on Data Collection and Data Preprocessing.md |
Guide on collecting, cleaning, and preparing data for ML |
python programming handwritten notes.pdf |
A PDF of handwritten notes for reference |
hello.py |
A basic Python script as a starter template |
readme.md |
You’re reading it 📘 |
-
Start with Python Basics →
🐍_Python_Getting_Started.ipynbandpython_basics.ipynb -
Explore OOP Concepts →
Object_Oriented_Programming_(OOP).ipynbandOop_Python_Notebook.ipynb -
Work with NumPy & Pandas →
NumPY.ipynb,🐼_Python_Pandas.ipynb, andPandas.ipynb -
Master Data Visualization →
Matplotlib_Visualization_with_Python.ipynbandMatplotlib_Seaborn.ipynb -
Understand Modules & Errors →
Modules_and_Libraries_in_Python.ipynb,Exception_Handling_in_Python.ipynb -
Dive into ML with Scikit-Learn →
Scikit_Learn_Machine_Learning_in_Python_.ipynb,TITANIC.ipynb, andiris(step_bystep).ipynb -
Read the Guide & Notes →
guide on Data Collection and Data Preprocessing.md→python programming handwritten notes.pdf
Lovnish Verma A passionate developer and educator in the field of Python, Data Science, Machine Learning, and Backend Development. I use these notebooks for teaching sessions, workshops, and personal experiments.
Have suggestions or want to contribute? Feel free to fork this repository and submit a pull request with improvements, fixes, or new notebooks!
This repository is licensed under the MIT License. Feel free to use, modify, and distribute with attribution.
For queries, collaborations, or feedback: 📧 [email protected]
Made with lots of ❤️...