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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
| Date | Stars |
|---|---|
| 2026-07-31 | 318 |
| 2026-08-06 | 318 |
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[](https://github.com/ayyucedemirbas/Machine-Learning-Pathway/blob/master/readme.tr.md)
# Zero-to-Hero Pathway for Machine Learning and Deep Learning
#### Phase 1: Getting Started with Programming & Machine Learning
1. **Python Basics:**
- Start by learning Python, the most widely used programming language in machine learning. You can use resources like:
- Codecademy's Python Course: https://www.codecademy.com/learn/learn-python-3
- Python.org's Official Tutorial: https://docs.python.org/3/tutorial/
- Harvard CS50’s Introduction to Programming with Python: https://cs50.harvard.edu/python/2022/
2. **Object-Oriented Programming (OOP):**
- Learn the fundamentals of OOP as it is commonly used in machine learning libraries and projects. Understand concepts like classes, objects, inheritance, and polymorphism.
- Python OOP Tutorial: https://realpython.com/python3-object-oriented-programming/
- freeCodeCamp Object Oriented Programming with Python: https://www.youtube.com/watch?v=Ej_02ICOIgs
#### Optional: Learning Git and Bash Basics
**Version Control with Git:**
- Understand the basics of version control with Git, including creating repositories, making commits, branching, and merging.
- GitHub and Git Tutorial for Beginners: https://www.datacamp.com/tutorial/github-and-git-tutorial-for-beginners
- Git Document: https://git-scm.com/book/en/v2
- W3 Schools Git Tutorial: https://www.w3schools.com/git/
**Bash Basics:**
- Learn the fundamentals of Bash scripting and command-line operations to automate tasks and manage your projects effectively.
- Bash Scripting Tutorial for Beginners: https://linuxconfig.org/bash-scripting-tutorial-for-beginners
3. **Mathematics for Machine Learning:**
- Brush up on essential mathematical concepts used in machine learning, such as linear algebra, calculus, and probability. You can use:
- Khan Academy's Linear Algebra Course: https://www.khanacademy.org/math/linear-algebra
- Khan Academy's Multivariable Calculus Course: https://www.khanacademy.org/math/multivariable-calculus
- Coursera's Mathematics for Machine Learning Specialization: https://www.coursera.org/specializations/mathematics-machine-learning
- A collection of resources to learn mathematics for machine learning: https://github.com/dair-ai/Mathematics-for-ML
4. **Discrete Mathematics:**
- Study discrete mathematics, which is important for understanding algorithms, data structures, and probability theory.
- MIT OpenCourseWare - Mathematics for Computer Science: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2005/
5. **Analysis of Algorithms:**
- Understand the fundamentals of algorithm analysis, time complexity, and space complexity, which are essential for optimizing machine learning models and algorithms.
- MIT OpenCourseWare - Introduction to Algorithms: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/
- Coursera - Algorithms Specialization from Stanford University: https://www.coursera.org/specializations/algorithms
6. **Introduction to Machine Learning:**
- Enroll in a beginner-level machine learning course that covers the following subtopics:
**Instance-Based Methods:**
- Learn about k-Nearest Neighbors (k-NN) algorithm and its applications.
- Introduction to k-Nearest NeighExcerpt of 18,145 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:909bb3fdc6b45d6d, llm:Repository name: 'Machine-Learning-Pathway'. No topics or README provided. Likely a learning resource/track for machine learning based on name.