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Python for ML

Python Jupyter License Last Commit

Welcome to my Python for Machine Learning learning repository.

This repository documents my hands-on journey from fundamental Python programming to machine learning-ready coding, utilizing Jupyter Notebooks, practice sets, and modular mini-projects.

Table of Contents

Repository Structure

Fundamentals of Python/

Core Python programming concepts:

  • Control flow (if-else statements, for loops, while loops, nested loops, loop control mechanisms)
  • Data structures (lists, tuples, sets, frozensets, dictionaries, strings)
  • String operations and practice exercises
  • Functions and arguments
  • Lambda functions, list comprehensions, and dictionary comprehensions
  • Sequence sum patterns
  • Modules and operators

Advance Python/

  • Decorators
  • Namespace and scope management

Exception Handling in Python/

  • Classification of Python errors
  • Implementation of try, except, else, and finally blocks
  • Creating and handling custom exceptions

File Handling in Python/

  • Text file operations (read, write, append) and context management using the with statement
  • Binary file operations
  • Serialization and deserialization
  • Pickling and unpickling Python objects

OOPS in Python/

Object-Oriented Programming principles:

  • Classes and objects (Part 1 & 2)
  • Reference variables and user-defined data types
  • Inheritance hierarchies
  • Encapsulation and data hiding
  • Abstraction
  • Polymorphism
  • Object aggregation and the super() function

Python Fundamental Questions/

Practice notebooks dedicated to:

  • Fundamental logic and level-1 problem solving
  • List and dictionary manipulation exercises
  • List comprehension practice
  • Decorator practicals
  • OOP practice
  • Exception handling practicals

My Projects/

Functional mini-projects developed during the learning phase:

  • Standard Calculator and Calculator V2
  • ATM System simulation
  • Library Management project
  • DinosaursPedia
  • Google Account Creation & Login simulation

NumPy For Python/

Fundamental array computing:

  • Array creation and attributes
  • Basic indexing and slicing
  • Iteration and array reshaping
  • Array stacking and splitting

NumPy For Practice/

Dedicated notebooks for reinforcing core NumPy concepts.

NumPy Advance/

In-depth exploration of advanced array operations and mathematical computing:

  • Advanced Indexing: Techniques for complex array selection and multi-dimensional slicing.
  • Array Broadcasting: Operational rules, implementation examples, and computational error resolution.
  • Handling Missing Values: Identification, filtering, and management of NaN/null data points within numerical arrays.
  • Plotting Graphs: Integrating array data with visualization operations.
  • Set Functions: Advanced operations including union, intersection, and unique value extraction on arrays.
  • Extra Methods (Part 1 & 2): Comprehensive coverage of specialized NumPy utility functions for extended statistical and mathematical operations.

Pandas in Python/

Comprehensive coverage of the Pandas library for data manipulation and analysis, organized into sub-folders:

Series in Pandas/

  • Creation and structural understanding (Part 1 & 2)
  • Indexing and slicing
  • Math methods
  • Plotting with built-in plot methods

DataFrames in Pandas/

  • Introduction, creation, and structural understanding
  • Filtering data and adding columns
  • Editing the index and using Python functionality within Pandas
  • Selecting columns, rows, and combined selections
  • Math methods and statistical operations
  • GroupBy: Aggregation, transformation, and group-level operations (Part 1 & 2), including hands-on exercises
  • Important DataFrame methods (reference notebook)

Practice in Pandas/

  • Real-world dataset practice (YouTube channel analytics)

Database from Kaggle/

A curated collection of real-world CSV datasets used across Pandas and NumPy notebooks:

Dataset Description
batsman_runs_ipl.csv IPL batsman run statistics
bollywood.csv Bollywood movie data
deliveries.csv IPL ball-by-ball delivery data
diabetes.csv Diabetes patient health metrics
global_top2000.csv Global top 2000 companies
imdb-top-1000.csv IMDB top 1000 movies
ipl-matches.csv IPL match results
kohli_ipl.csv Virat Kohli IPL performance stats
movies.csv General movies dataset
subs.csv Subscriber data

Tech Stack

Tool Purpose
Python 3 Core programming language
Jupyter Notebook Interactive development environment
NumPy Numerical computing
Pandas Data manipulation and analysis
Matplotlib Data visualization
Git & GitHub Version control and hosting

Goal

To establish a robust foundation in Python programming tailored for Data Science and Machine Learning, bridging the gap between theoretical syntax and real-world analytical projects.

Maintained by Ayush Suthar