This repository contains code and examples for a 17-day Python for Machine Learning course. Each day covers a different topic related to Python and Machine Learning, with mini-projects to help reinforce your learning.

Day 1: Introduction to Python & How to Start with Python? Variables in Python, Strings in Python, String Formatting in Python

Day 2: List in Python, Dictionaries in Python, Tuples, Set, & Comparison Operators in Python, Conditional Statements

Day 3: For Loop in Python, While Loop in Python, Functions in Python, Python - Problem Solving 1, Python - Problem Solving 2

Day 4: Python Operators - input, enumerate, zip, range, min, max, random, List Comprehensions, Guessing Game Challenge - Mini Project, List Methods - append, count, extend, insert, pop, remove, Python - Problem Solving 3

Day 5: Lambdas, Map, & Filter, Arguments & Keyword Arguments, Exception Handling, Python - Problem Solving 4

Day 6: Classes and Objects, Inheritance in Python, Polymorphism, URL Shortener using Python - Mini Project

Day 7: Create and Read QR Code using Python - Mini Project, Convert any PDF to AudioBook - Mini Project, Spell Correction using Python - Mini Project, Emotion Detector using Python - Mini Project

Day 8: Numpy Arrays, Numpy Indexing, Numpy Operations

Day 9: Pandas - Series, Pandas - DataFrame, Pandas - Missing Data, Groupby, Pandas - Operations

To run the code in this repository, you will need to install Python 3 and several Python libraries, including NumPy and Pandas. You can install these libraries using the following command:

pip install numpy pandas

Each day's code is contained in a separate Jupyter Notebook file. To run a notebook, simply open it in Jupyter and execute the code cells. The notebooks are designed to be self-contained, so you should be able to run them without any additional setup.