Your comprehensive guide to mastering Python for AI/ML interviews
Welcome to my Python prep for AI/ML interviews! 🚀 This repository is your essential guide for mastering Python, the backbone of AI and data science, with hands-on coding and interview-focused practice. From core fundamentals to advanced techniques, it’s crafted to help you shine in technical interviews and AI projects with clarity and confidence.
- Core Python Mastery: Dive deep into data structures, control flow, OOP, and more to ace coding tests.
- AI/ML Libraries: Explore NumPy, Pandas, Matplotlib, Scikit-learn, and other key toolkits.
- Hands-on Practice: Solve curated coding problems with detailed solutions to sharpen your edge.
- Interview Question Bank: Tackle common questions with clear, concise answers.
- Performance Optimization: Learn tips for writing efficient, interview-ready Python code.
- Data Scientists prepping for technical interviews.
- Machine Learning Engineers strengthening Python foundations.
- AI Researchers enhancing coding efficiency.
- Software Engineers transitioning to AI/ML roles.
- Anyone mastering Python for data-centric applications.
- Lists
- Dictionaries
- Tuples
- Sets
- Strings
- Frozen Sets
- If-Else Statements
- Elif Statements
- Nested Conditionals
- For Loops
- While Loops
- Break Statement
- Continue Statement
- Pass Statement
- Defining Functions
- Positional Arguments
- Keyword Arguments
- Default Parameters
- Variable-Length Arguments (*args, **kwargs)
- Lambda Expressions
- Recursion
- Function Annotations
- List Comprehensions
- Dictionary Comprehensions
- Set Comprehensions
- Generator Expressions
- Try-Except
- Multiple Except Blocks
- Else Clause
- Finally Clause
- Raise Statement
- Custom Exceptions
- Classes and Objects
- Attributes
- Methods
- Inheritance
- Multiple Inheritance
- Encapsulation
- Polymorphism
- Abstract Classes
- Static Methods
- Class Methods
- Importing Modules
- Creating Modules
- Packages
- Standard Library
- math
- random
- datetime
- os
- sys
- time
- Reading Files
- Writing Files
- CSV Files
- Iterators
- Generators
- Yield Statement
- Function Decorators
- Class Decorators
Python is the go-to language for AI/ML, and here’s why:
- Versatility: Powers the full AI workflow—from data cleaning to deployment.
- Rich Ecosystem: Packed with libraries like NumPy and Scikit-learn.
- Readability: Clean syntax boosts focus on problem-solving.
- Industry Demand: A must-have skill for 6 LPA+ AI/ML roles.
- Community Support: Tap into a huge network of experts.
This repo is my roadmap to mastering Python for technical interviews and AI/ML careers—let’s build that skill set together!
- Week 1-2: Core Python Fundamentals
- Week 3-4: Data Structures and Algorithms
Love to collaborate? Here’s how! 🌟
- Fork the repository.
- Create a feature branch (
git checkout -b feature/amazing-addition
). - Commit your changes (
git commit -m 'Add some amazing content'
). - Push to the branch (
git push origin feature/amazing-addition
). - Open a Pull Request.
Happy Learning and Good Luck with Your Interviews! ✨