Skip to content

ktirupathi/Python_3

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

14 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Python for Machine Learning & AI Engineers — Complete Interview Preparation

A complete 10-week learning path designed for Python-heavy ML and AI Engineering interviews.

Learning Roadmap

Beginner Python → Core data structures → Functional abstraction → OOP → Advanced Python → NumPy/Pandas → ML Python scenarios → Python system design → Interview mastery.

Weekly Plan

  • Week 1: Python Basicsweek-1-basics/README.md + assignments + solutions
  • Week 2: Data Structuresweek-2-data-structures/README.md + assignments + solutions
  • Week 3: Functions & Lambdaweek-3-functions/README.md + assignments + solutions
  • Week 4: OOP in Pythonweek-4-oop/README.md + assignments + solutions
  • Week 5: Advanced Pythonweek-5-advanced/README.md + assignments + solutions
  • Week 6: NumPy for MLweek-6-numpy/README.md + assignments + solutions
  • Week 7: Pandas for MLweek-7-pandas/README.md + assignments + solutions
  • Week 8: Python for ML Scenariosweek-8-ml-python/README.md + assignments + solutions
  • Week 9: Python System Design for MLweek-9-system-design/README.md + assignments + solutions
  • Week 10: Interview Preparationweek-10-interview-prep/README.md + assignments + solutions

System Design Section

  • Python-first ML system design prompts in system-design/README.md.

Interview Preparation Section

  • 250+ categorized questions in interview-questions/question-bank.md.
  • Weekly mock interview progression in week-10-interview-prep/README.md.

Progress Tracker

Week Topic Done Notes
1 Python Basics
2 Data Structures
3 Functions & Lambda
4 OOP in Python
5 Advanced Python
6 NumPy for ML
7 Pandas for ML
8 Python for ML Scenarios
9 Python System Design for ML
10 Interview Preparation

Contribution Guide

  1. Fork and create a topic branch.
  2. Add concept depth, interview scenarios, and runnable code examples.
  3. Ensure each new concept includes explanation, edge cases, mistakes, performance notes, and interview tips.
  4. Add/expand assignments and solutions with expected output and hints.
  5. Open a PR with before/after improvements and verification commands.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors

Languages