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A complete A-Z guide to Machine Learning and Data Science using Python. Includes implementation of ML algorithms, statistical methods, and feature selection techniques in Jupyter Notebooks. Follow Coursesteach for tutorials and updates.
Complete A-Z Machine Learning & Data Science guide with algorithm implementations, statistical methods, and feature selection in Jupyter Notebooks Machine Learning Algorithms in Python, Complete ML Guide Tutoria, Data Science Implementation Examples, Scikit-learn Jupyter Notebooks, ML Feature Selection Techniques,Supervised Learning Classification Regression" - Core ML concepts
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.
Your support helps us:
You can support this project by becoming a sponsor on GitHub Sponsors or via bank transfer — please contact me at 📧 mushtaqmsit@gmail.com.
Every contribution — big or small — helps sustain the development of open-source Python learning materials, AI-driven educational resources, and data science tools.
Thank you for your generous support! 🌟
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.
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
Please enrolled in the following courses to strengthen knowledge and practical skills in Machine Learning. These courses are designed to provide both theoretical understanding and hands-on experience with real-world ML applications.
🔗 Fundmental of Machine Learningl
1- Covers foundational concepts such as Classification, Regression, Clustering, Recommendation ,Neural network ,Support Vector Machine ETC
🔗 Sklearn in Supervised Learning
1- Covers foundational concepts such as Classification With sklearn, Regression with sklearn etc.
🔗 Dev.to
🔗 Gumroad
📬 Need Help? Connect with us on WhatsApp
| Topic Name/Tutorial | Video | Video | collaboration doc | Extra Resources |
|---|---|---|---|---|
| ✅1- Introduction to Artificial Intelligence (AI)-g⭐️ | 1-2-2 | Content 3 | Link | 1-2 |
| ✅2- What is machine learning-g? | 1-2-3-4-5 | -6-7 | --- | 1 |
| Topic Name/Tutorial | Video | Code | Extra Reading |
|---|---|---|---|
| ✅Model Representation | 1-2-3 | --- | |
| **✅1-Simple Linear Regression u |
| ✅3-Types of Machine Learning-g?⭐️ | 1-2-3 | 1 |
| ✅4-Steps involved in Building a Machine Learning Model⭐️ | 1-2-3 | --- |
| ✅5-Statistics vs Machine Learning vs Data Mining⭐️ | 1 | --- |
| ✅5-Best Free Resources to Learn Machine Learning⭐️ | --- | --- |