This repository provides a structured, chronological learning path for high school students interested in Machine Learning and Artificial Intelligence. It aims to make the field accessible without requiring advanced university-level mathematics, offering a three-month roadmap to proficiency and resources for continued learning.

How It Works

The guide progresses through five key stages: foundational Python programming and essential libraries (NumPy, Pandas, Matplotlib), understanding core ML concepts via Andrew Ng's course, implementing algorithms with practical examples, independent project work on curated datasets, and finally, specializing in areas like Computer Vision, NLP, or Reinforcement Learning. It emphasizes conceptual understanding over deep mathematical proofs, suggesting resources that balance theory with hands-on application.

Quick Start & Requirements

• Primary Install/Run: Not applicable; this is a learning guide, not a software package.
• Prerequisites: Basic computer literacy. Recommended: Python 3.x.
• Resources: Links to free and paid online courses (edX, Coursera, Udemy), tutorials, datasets (Kaggle, UCI), and community forums (Stack Overflow, Reddit).
• Estimated Setup: Self-paced, with a suggested 3-month timeline for basic proficiency.

Highlighted Details

• Focuses on Python and its core ML libraries (NumPy, Pandas, Matplotlib).
• Recommends Andrew Ng's ML course, advising to focus on intuition over complex math.
• Suggests practical implementation courses and platforms like Kaggle for project-based learning.
• Includes a "Bonus" section on broader AI/ML implications, research papers, and following industry pioneers.

Maintenance & Community

The project is maintained by a high school student, Karan. Contributions and feedback are welcomed via Pull Requests or direct email. Community engagement is encouraged through platforms like Stack Overflow and Reddit.

Licensing & Compatibility

The repository itself is likely under a permissive license (e.g., MIT, Apache) given its nature as a guide, but specific linked resources may have their own terms. Compatibility is broad, focusing on standard programming environments.

Limitations & Caveats

While aiming to minimize math prerequisites, some recommended courses (e.g., Andrew Ng's) may still touch upon university-level concepts that could be challenging for some students. The guide relies heavily on external resources, whose availability or pricing may change.