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The A-Z Guide to Machine Learning is a comprehensive resource designed to cater to both beginners and experienced practitioners in the field of Machine Learning. Whether you're just starting your journey into ML or seeking to deepen your understanding and refine your skills, this repository has something for everyone.
Extensive Algorithm Coverage: Explore a wide range of ML algorithms, including but not limited to linear regression, decision trees, support vector machines, neural networks, clustering techniques, and more.
1- Hands-On Implementations: Dive into practical implementations of these algorithms in Python, alongside explanations and insights into their workings.
2- Code Examples and Jupyter Notebooks: Access code examples and Jupyter notebooks that provide step-by-step guidance, making it easier to grasp complex concepts and experiment with different techniques.
3- Supplementary Resources: Discover additional resources, such as articles, tutorials, and datasets, to supplement your learning and enhance your understanding of Machine Learning principles and applications.
4- Contents Algorithms: Implementation examples of various ML algorithms, organized for easy navigation and reference.
5- Techniques: Practical demonstrations of ML techniques, such as feature engineering, model evaluation, hyperparameter tuning, and more.
We believe that the most effective learning and growth happen when people come together to exchange knowledge and ideas. Whether you're an experienced professional or just beginning your machine learning journey, your input can be valuable to the community. We welcome contributions from the community! Whether it's fixing a bug, adding a new algorithm implementation, or improving documentation, your contributions are valuable. Please contact on my skype ID: themushtaq48 for guidelines on how to contribute.
- Introduction of Python (Variable, Loop etc)
- Basic Probability Theory (Expectations and Distributions)
- Multivariate Calculus
1- Share Your Expertise: If you have knowledge or insights in machine learning or TinyML, your contributions can assist others in learning and applying these concepts.
2-Enhance Your Skills: Contributing to this project offers a great opportunity to deepen your understanding of machine learning systems. Writing, coding, or reviewing content will reinforce your knowledge while uncovering new areas of the field.
3- Collaborate and Connect: Join a community of like-minded individuals committed to advancing AI education. Work with peers, receive feedback, and build connections that may open up new opportunities.
4- Make a Difference: Your contributions can shape how others learn and engage with machine learning. By refining and expanding content, you help shape the education of future engineers and AI experts.
🚀 Fork & Star this repository
👩💻 Explore and Learn from structured lessons
🔧 Enhance the current blog or code, or write a blog on a new topic
🔧 Implement & Experiment with provided code
🔧Convert lessons into interactive Colab notebooks
🤝 Collaborate with fellow ML enthusiasts
🔧 Add new tutorials
🔧 Add quizzes or solutions
🔧 Create blog from next topic in our jounrney
🔧 suggestion other important website ,repistory,youtube Channel etc
📌 Contribute your own implementations & projects
📌 Share valuable blogs, videos, courses, GitHub repositories, and research websites
🔗 Gumroad
📬 Need Help? Connect with us on WhatsApp
🙏 Special thanks 🙏 to our Virtual University of Pakistan students, reviewers, and content contributors, notably Dr Said Nabi
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| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-Calculus for Machine Learning: Building Blocks for Data Science | Content 2 | Content 3 |
| 🌐2- Introduction to Functions | Video-Video2 | Content 6 |
| 🌐3-How Calculus is useful | Video1 | --- |
| 🌐4-Understanding Derivative in Machine Learning: A Key Concept for Algorithm Optimization | Video1-Video2 | --- |
| 🌐5-Differentiation examples & special cases | Video1 | |
| 🌐6-Product rule | Video | --- |
| 🌐7-Chain rule | Video-Video2-Video3 | --- |
| 🌐8-Taming a beast | 1 | --- |
| No. | Title/Link | Description | Reading Status | University / Platform | Feedback |
|---|---|---|---|---|---|
| 1 | Learn Calculus by Coding in Python | By Beau Carnes, Coursera | In Progress | freeCodeCamp | ⭐️⭐️⭐️⭐️ |
| 2 | Machine Learning | A free course from Google | Pending | ||
| 3 | Machine Learning from Scratch - Python | By Patrick Loeber (YouTube) | Pending | YouTube | |
| 4 | Machine Learning Zoomcamp | A free 4-month course on ML engineering | Pending | DataTalks.Club | |
| 5 | Stanford CS229: Machine Learning | Full course taught by Andrew Ng | Pending | Stanford | |
| 6 | Google Machine Learning Education | Google's dedicated ML learning hub | Pending | ||
| 7 | StatQuest: Machine Learning | Easy-to-understand ML explained with stats | Pending | StatQuest (YouTube) | |
| 8 | PreCalculus - Math for ML | By Dr. Trefor Bazett (Great math fundamentals) | Pending | YouTube | |
| 9 | Machine Learning with Graphs | Covers GNNs and graph-based ML | Pending | Stanford | |
| 10 | MIT RES.LL-005 Mathematics of Big Data and ML | In-depth mathematical foundations | Pending | MIT | |
| 11 | CS294-158 Deep Unsupervised Learning SP19 | Covers deep learning and generative models | Pending | UC Berkeley | |
| 12 | Introduction to Machine Learning | By Dmitry (University of Tübingen) | Pending | University of Tübingen | |
| 13 | Statistical Machine Learning - 2020 | By Ulrike von Luxburg | Pending | University of Tübingen | |
| 14 | Probabilistic Machine Learning - 2020 | By Philipp Hennig | Pending | University of Tübingen | |
| 15 | Machine Learning Concepts | github websit it implement all concept in sklearn | Pending | Github | ⭐️⭐️⭐️ |
| 16 | Singular Value Decomposition | Steve Brunton | Pending | Youtub | |
| 17 | Linear Algebra for Machine Learning | Jon Krohn | Pending | Youtub | ⭐️⭐️⭐️ |
| 18 | Learning from Data | Taught by Feynman Prize winner Professor Yaser Abu-Mostafa. | Youtub | ⭐️⭐️⭐️ | |
| 19 | UC Berkeley CS188 Intro to AI | Complete sets of Lecture Slides and Videos | Youtub | ⭐️⭐️⭐️ |
| Title | Description | Status |
|---|---|---|
| ✅ 1-Roadmap.sh | Comprehensive roadmap for AI courses | Completed |
| ✅ 2-Bolt | Write software code and deploy | Completed |
| ✅ 3-AI Personal Assistant | Write software code and deploy | Completed |
| ✅ 4-Deep-ML | Interactive learning of ML, solve ML problems | Completed |
| ✅ 5-LeetGPU | It offers real-time execution and GPU simulation for learning and performance analysis. | InProgress |
| Title/Link | Description | Status | Platform |
|---|---|---|---|
| ✅ 1- HELP ME CROWD-SOURCE A MACHINE LEARNING ROADMAP - 2025 | Reddit thread focused on crowd-sourcing a 2025 ML learning roadmap | Pending | |
| ✅ 2- Introductory Books to Learn the Math Behind Machine Learning (ML) | Community recommendations for foundational ML math books | Pending | |
| ✅ 3- Industry ML Skill | Substack publication sharing ML skills in industry settings | Pending | Substack |
| ✅ 4- Data School | YouTube channel focused on teaching Scikit-learn and data science | Pending | Youtube |
| Title/Link | Description | Code |
|---|---|---|
| ✅ 1- Linear Algebra and Optimization for Machine Learning | Videos and GitHub resources for learning | Not provided |
| ✅ 2- The-Art-of-Linear-Algebra | Videos and GitHub resources for learning | Not provided |
| Title/Link | Description | Status |
|---|---|---|
| ✅ 1- Computer Science Courses with Video Lectures | GitHub repository with video lectures for computer science courses | Pending |
| ✅ 2- ML YouTube Courses | GitHub repository containing YouTube courses on machine learning | Pending |
| ✅ 3- ML Roadmap | GitHub repository for machine learning roadmap | Pending |
| ✅ 4- Courses & Resources | GitHub repository with AI courses and resources | Pending |
| ✅ 5- Awesome Machine Learning and AI Courses | GitHub repository featuring a curated list of machine learning and AI courses | Pending |
| ✅ 6- Feature Engineering and Feature Selection | GitHub repository focused on feature engineering and selection in Python by Yimeng Zhang | Pending |
| ✅ 7- machine-learning | This is a continuously updated repository that documents personal journey on learning data science, machine learning related topics basci to advance level implementationm and topic | Pending |
| Title | Description | Code |
|---|---|---|
| 🌐1- Prompt Library | Find Prompt | --- |
| 🌐2- Computer Science courses w | It is Videos and github | --- |
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Fork the repository
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Clone your forked repository using terminal or gitbash.
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Make changes to the cloned repository
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Add, Commit and Push
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Then in Github, in your cloned repository find the option to make a pull request
print("Start contributing for Machine Learning")
- Make sure you do not copy codes from external sources because that work will not be considered. Plagiarism is strictly not allowed.
- You can only work on issues that have been assigned to you.
- If you want to contribute the algorithm, it's preferrable that you create a new issue before making a PR and link your PR to that issue.
- If you have modified/added code work, make sure the code compiles before submitting.
- Strictly use snake_case (underscore_separated) in your file_name and push it in correct folder.
- Do not update the README.md.
🔍 Explore more👋🛒
Explore cutting-edge tools and Python libraries, access insightful slides and source code, and tap into a wealth of free online courses from top universities and organizations. Connect with like-minded individuals on Reddit, Facebook, and beyond, and stay updated with our YouTube channel and GitHub repository. Don’t wait — enroll now and unleash your Machine Learning potential!”
We would love your help in making this repository even better! If you know of an amazing AI course that isn't listed here, or if you have any suggestions for improvement in any course content, feel free to open an issue or submit a course contribution request.
Together, let's make this the best AI learning hub website! 🚀
Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀