This repository contains a collection of Jupyter notebooks that I created as part of my journey in learning machine learning and data science. Each notebook focuses on different fundamental libraries and concepts, serving as a practical guide to the basics of machine learning and data visualization.
- NumPy Basics (
NumPybasics.ipynb): An introduction to NumPy, showcasing array operations, indexing, and basic functionalities. - OS Module in Python (
OS module python.ipynb): Demonstrates the usage of Python's OS module for interacting with the operating system. - Big Mart Sales Analysis (
bigmartsales.ipynb): A simple machine learning project analyzing Big Mart's sales data. - Matplotlib Basics (
matplotlib basics.ipynb): Introduction to Matplotlib for data visualization, covering basic plotting techniques. - Pandas Basics (
pandas basics.ipynb): Exploring Pandas for data manipulation and analysis, covering its core functionalities. - Seaborn Basics (
seaborn basics.ipynb): Basics of Seaborn, a Python visualization library, for creating informative statistical graphics. - Simple ML Project (
simple ml project.ipynb): A basic machine learning project, applying fundamental concepts and models.
The purpose of these notebooks is to provide a hands-on approach to learning data science and machine learning concepts. They are intended for beginners who are just starting out in the field and serve as a reference for basic techniques and library usage.
Feel free to explore, use, and adapt these notebooks for your learning journey!