Welcome to my Machine Learning Journey repository! This project documents my step-by-step exploration of machine learning and artificial intelligence. As a Python developer and aspiring AI engineer, I'm sharing my code, projects, and experiences in this repository to inspire others and track my personal growth.
Also, I share my progress and updates on LinkedIn. Feel free to connect and follow along!
- Roadmap: A comprehensive 5-phase learning plan covering prerequisites, programming, AI fundamentals, and advanced topics like NLP, Computer Vision, and Reinforcement Learning.
- Well-Organized Code: Clean and structured Python scripts, Jupyter Notebooks, and real-world projects categorized by learning phases.
- Explanations: Each section includes markdown notes and comments to explain concepts and techniques in depth.
- Resources: Links to tutorials, articles, and courses that have helped me along the way.
Understanding the mathematical foundations of machine learning, including:
- Calculus
- Linear Algebra
- Probability and Statistics
Mastering the programming basics required for machine learning:
- Python basics
- Data structures and algorithms
- Essential Python libraries: NumPy, Pandas
Building hands-on experience with machine learning models and AI frameworks:
- Working with TensorFlow and PyTorch
- Developing machine learning solutions
A real-world machine learning project that integrates all the skills learned in the previous phases.
Exploring specialized machine learning topics:
- Natural Language Processing (NLP)
- Computer Vision
- Reinforcement Learning
This repository is not just a collection of code—it's a personal learning log, a resource for anyone starting their own ML journey, and a portfolio to showcase my skills to potential collaborators and employers.