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Your support helps us:

• Create free tutorials, guides, and datasets for learners worldwide 🌍

• Maintain research repositories and AI projects 📚

• Host workshops, webinars, and community learning events 💬

• Encourage students and educators to explore Data Science, Machine Learning, and Deep Learning 🚀

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🙌 Become a Sponsor

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! 🌟

What You’ll Learn

• 📊 Data Preprocessing: Cleaning, encoding, scaling, handling missing values

• 📈 Supervised Learning: Linear regression, logistic regression, SVMs, decision trees

• 🧠 Unsupervised Learning: Clustering with K-Means, PCA, hierarchical clustering

• 🧪 Model Evaluation: Confusion matrix, cross-validation, precision, recall

• ⚙️ Scikit-Learn Pipeline: Automating ML workflows

• 🧹 Feature Engineering: Selection, extraction, and transformation

• 🛠️ Project-based learning: Mini-projects to apply ML to real-world datasets

Features👋🛒

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.

Contributing🙌

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.

Prerequisites📋

• Introduction of Python (Variable, Loop etc)

• Basic Probability Theory (Expectations and Distributions)

• Multivariate Calculus

Quick Start Checklist with Links

• Subscribe to Couresesteach on YouTube and review the course playlist.

• Read and Contribute in Machine Learning Notes Machine Learning Notes.

• Enroll in Complete Machine Learning Courese Machine Learning! to Solve quiz and find extr resources.

Why 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.

💡 How to Participate?

🚀 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

🎓 Enrolled Courses

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.

🌍 Join Our Community

🔗 YouTube Channel 🔗 Dev.to 🔗 Facebook 🔗 LinkedIn 🔗 Gumroad 📬 Need Help? Connect with us on WhatsApp

Course 01 - ⚙️Machine Learning

Course

• Machine Learning Crash Course by Google - FREE

Github

• Machine Learning Crash Course by Miscrofot - FREE

Blog Webite

• MLAcademy - FREE

📚Chapter: 1 - Introduction

| 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| |✅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⭐️|---|---| ## 📚Chapter: 2 -Linear Regression with one Variable |Topic Name/Tutorial | Video | Code |Extra Reading| |---|---|---|---| |✅Model Representation|1-2-3|---| | ✅1-Simple Linear Regression using sklearn(Lab1)| --- || | ✅2-Simple Linear Regression with python-Andrew | --- || |✅Understanding the Linear Regression Cost Function|1||1| |✅What the cost function is doing?|1-2|| |✅Understanding Gradient Descent|1-2-3-4-5||1-3-4-5-6| |✅Gradient Descent For Linear Regression|1-2-3||1| |Newton Raphson method|1|| ## 📚Chapter: 3 -Linear Algebra | Topic Name/Tutorial | Video | Code |Extra Resources| |---|---|---|---| | ✅1-Understanding Matrices and Vectors in Linear Algebra | 1-2 | |1-2| | ✅2-Understanding Addition and Scalar Multiplication of Matrices-S |1-2 | |1-2| |✅3-Matrix-Vector Multiplication-s|1-2|| |✅4-Matrix-Matrix Multiplication-s| 1-2|| |✅5-Matrix multiplication Properties-S|1-2|| |✅6-Inverse and Transpose-s|1-2|| |✅-Eigenvalues and Eigenvectors|1|| |✅-Derivative of a Matrix|1|| |✅-Metric Spaces|1|| |✅-Why Vectors Are EVERYTHING in Machine Learning|1|| |✅-Learn Linear Algebra for AI (Beginner Guide)|1||

Books

• Machine Learning Notebooks, 3rd edition - FREE

Github

• Learn Linear Algebra - FREE

## 📚Chapter:3 -Statistical and Probability |Topic Name/Tutorial | Video | Code |Note|Extra Resources| |---|---|---|---|---| |✅1-Random variables and probability distribution|1-2-3||Doc|1-2| |🌐2-Maximum Likelihood|1-2||Doc|---| |🌐3-Central Tendency|1-2||Doc|---| |🌐4-How to Calculate Similarity in Data|1||Doc|---| ## 📚Chapter:4 -Calculus |Topic Name/Tutorial | Video | Code |Note|Extra Resources| |---|---|---|---|---| |🌐1-Understand abouTslope|1||Doc|--| ## 📚Chapter: 4 -Linear Regression with Multiple Variable | Topic Name/Tutorial | Video | Code | |---|---|---| |✅1-Multiple Features(multivariate linear regression)-s|1-2|| |✅2-Gradient Descent for Multiple Variables=S|1-2|| |✅3-Gradient Descent in Practice I — Feature Scaling-s|1-2|| |✅4-Gradient Descent in Practice II — Learning Rate|1-2|| |✅5-Features and Polynomial Regression|1-2|| |✅6-Normal Equation|1-2||

📚Chapter: 5 -Logistic Regression

|Topic Name/Tutorial | Video | Code | |---|---|---| |✅1-Classification|1|| |✅2-Hypothesis Representation of Logistic Regression|1-2|| |✅3-Decision Boundary⭐️|1|| |✅4-The Cost Function in Logistic Regression|1-2|| |✅5-Simplified Cost Function and Gradient Descent|1|| |✅6-Advanced Optimization|1|| |*✅7-Multiclass Classification — One-vs-all|1-2|| |✅8-Difference Between Linear Regression and Logistic Regression|1|--|

📚Chapter: 6 -Regularization

|Topic Name/Tutorial | Video | Code |Extra Learning| |---|---|---|---| |✅1-The problem of overfitting|1-2|| |✅2-Cost Function and Regularization|1|| |✅3-Regularized Linear Regression|1|| |✅4-Regularized Logistic Regression|1||

📚Chapter: 7 -Neural Network Representation

|Topic Name/Tutorial | Video | Code | |---|---|---| |🌐1-Non-1linear Hypotheses|1|| |🌐2-The Science Behind Neural Networks: Exploring|1|| |🌐3- Model Representation 2|1-2|| |🌐4- Examples and Intuitions I|1|| |🌐5- Computing Complex Nonlinear Hypotheses|1|| |🌐6-Using Neural Networks for Multiclass Classification|1||

📚Chapter: 8 -Neural Network Learning

|Topic Name/Tutorial | Video | Code |Extra Learning| |---|---|---|---| |🌐1-Cost Function⭐️|1|| |🌐2-Backpropagation⭐️|1|| |🌐3-Backpropagation intuition⭐️|1-2|| |🌐4-Implementation Note - Unrolling Parameters⭐️|1|| |🌐5-Gradient Checking⭐️|1|| |🌐6-Random Initialization⭐️|1|| |🌐7-Putting it togather⭐️|1|| |🌐8-Autonomous Driving⭐️|1||

📚Chapter: 9 -Model Selection

|Topic Name/Tutorial | Video | Code |Podcast|Note| |---|---|---|---|---| |🌐1-Deciding What to Try Next⭐️|1|| |🌐2-Evaluating a Hypothesis⭐️|1|| |🌐3-Model selection and training/validation/test sets⭐️|1|| |🌐4-Diagnosing Bias vs. Variance⭐️|1||Podcast| |🌐5-Learning Curves⭐️|1||Podcast|Link| |🌐6-Deciding What to Do Next Revisited⭐️|1||Podcast|Link| |🌐7-Prioritizing What to Work On⭐️|1||Podcast|Doc| |🌐8-Error Analysis|1||---|Doc| |🌐9-Error Metrics for Skewed Classes|1||---|Doc| |🌐10-Trading Off Precision And Recall|1||---|Doc| |🌐11-Data For Machine Learning|1||---|Doc|

📚Chapter: 9 -SVM

|Topic Name/Tutorial | Video | Code |Note|Extra Resources| |---|---|---|---|---| |🌐1-Optimization Objective|1-2||Doc|1| |🌐2-Large Margin Intuition|1-2||Doc| |🌐3-Mathematics Behind Large Margin Classification|1-2||Doc| |🌐4-Kernels I|1-2-3||Doc| |🌐5-Kernels I1|1-2||Doc| |🌐6-Using An SVM|1-2||Doc|

📚Chapter: 10 -Unsupervised Learning

Youtube

• Dimensionality Reduction - FREE

|Topic Name/Tutorial | Video | Code |Note|Extra Resources| |---|---|---|---|---| |🌐1-Unsupervised Learning: Introduction|1-2||Doc|1| |🌐2-KMeans Algorithm|1-2||Doc|1| |🌐3-Optimization Objective|1-2||Doc|1| |🌐4-Random Initialization|1-2||Doc|---| |🌐5-Choosing the Number of Clusters|1-2||Doc|---| |🌐6-Motivation I Data Compression|1-2||Doc|---| |🌐7-Motivation II: Visualization|1-2||Doc|---| |🌐8-Principal Component Analysis Problem Formulation|1-2||Doc|---| |🌐9-Principal Component Analysis Algorithm|1-2||Doc|---| |🌐10-Choosing the Number of Principal Components|1-2||Doc|---| |🌐11-Reconstruction from Compressed Representation|1-2||Doc|---| 🌐12-Advice for Applying PCA|1-2||Doc|---|

📚Chapter: 12 -Anomaly Detection

|Topic Name/Tutorial | Video | Code |Note|Extra Resources| |---|---|---|---|---| |🌐1-Problem Motivation|1-2||Doc|1| |🌐2-Gaussian Distribution|1-2||Doc|---| |🌐-Outlier Detection: The Different Types of Outliers|1||Doc|---| |🌐3-Algorithm|1-2||Doc|---| |🌐5-Developing and Evaluating an Anomaly Detection System|1-2||Doc|--| |🌐6-Anomaly Detection Vs Supervised Learning|1-2||Doc|---| |🌐7-Choosing What Features to Use|1-2||Doc|---| |🌐8-Multivariate Gaussian Distribution|1-2||Doc|---| |🌐9-Anomaly Detection using the Multivariate Gaussian Distribution|1-2||Doc|---|

📚Chapter: 13 -Recommender Systems

|Topic Name/Tutorial | Video | Code |Note|Extra Resources| |---|---|---|---|---| |🌐1-Problem Formulation|1-2||Doc|1| |🌐2-Content-Based Recommendations|1-2||Doc|1| |🌐3-Collaborative Filtering Basic|1-2||Doc|1| |🌐4-Collaborative Filtering Algorithem|1-2||Doc|1| |🌐5-Vectorization Low-Rank Matrix Factorization|1-2||Doc|---| |🌐6-Implementational Detail: Mean Normalization|1-2||Doc|---|

📚Chapter: 14 -Large scale machine learning

|Topic Name/Tutorial | Video | Code |Note|Extra Resources| |---|---|---|---|---| |🌐1-Learning with Large Datasets|1-2||Doc|---| |🌐2-Stochastic Gradient Descent|1-2||Doc|---| |🌐3-Mini Batch Gradient Descent|1-2||Doc|---| |🌐4-stochastic Gradient Descent Convergence|1-2||Doc|---| |🌐5-Online Learning|1-2||Doc|---| |🌐6-Map Reduce And Data Parallelism|1-2||Doc