Python for ML

Welcome to my Python for Machine Learning learning repository.

This repo is my hands-on journey from Python basics to ML-ready programming using Jupyter notebooks, practice sets, and mini projects.

What this repository contains

Fundamentals of Python/

Core Python basics:

• Control flow (if-else, loops, loop control)
• Data structures (list, tuple, set, frozenset, dictionary, string)
• Functions and arguments
• Lambda and comprehensions
• Modules and operators

Advance Python/

• Decorators
• Namespace and scope

Exception Handling in Python/

• Error types in Python
• try / except / else / finally
• Custom exceptions

File Handling in Python/

• Text and binary file operations
• with statement usage
• Serialization/deserialization
• Pickling

OOPS in Python/

• Classes and objects
• Inheritance
• Encapsulation
• Abstraction
• Polymorphism
• Aggregation and super()

Python fundamental Questions/

Practice notebooks focused on:

• Fundamentals revision
• Data structure exercises
• Decorator, OOP, and exception handling practice

My projects/

Mini projects built during learning:

• Calculator
• Calculator V2
• ATM project
• Library project
• DinosaursPedia
• Google create & login

NumPy For Python /

NumPy basics:

• Arrays and attributes
• Indexing and slicing
• Iteration and reshaping
• Stacking and splitting

NumPy For Practice/

• Practice notebook for NumPy concepts

NumPy Advance/

Advanced NumPy topics:

• Advanced indexing
• Broadcasting
• Missing values
• Plotting and extra methods

Pandas in Python/

• DataFrame basics in Pandas

Tech stack

• Python 3
• Jupyter Notebook
• Git & GitHub

Goal

Build a strong Python base for Data Science and Machine Learning and move toward real-world ML projects.

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Maintained by Ayush