Welcome to the Machine Learning with Python repository — a hands-on, beginner-to-advanced level guide designed for students, educators, and self-learners. This open-source collection of Jupyter Notebooks, exercises, and datasets walks you through real-world machine learning workflows using Python, NumPy, Pandas, and Scikit-learn.
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.
- What You’ll Learn
- Features
- Contributing
- Prerequisites
- Contributing
- Contact
- Getting Started-Course 01
- Useful Python Resources
- 📊 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
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
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.
🔗 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 | Video | collaboration doc |
|---|---|---|---|
| ✅1- Introduction to Artificial Intelligence (AI)-g⭐️ | 1-2-2 | Content 3 | Link |
| ✅2- What is machine learning-g? | 1-2-3-4-5 | -6-7 | |
| ✅3-Types of Machine Learning-g?⭐️ | 1-2-3 | --- | |
| ✅4-Steps involved in Building a Machine Learning Model⭐️ | 1-2 | --- | |
| ✅5-Best Free Resources to Learn Machine Learning⭐️ | --- | --- |
| Topic Name/Tutorial | Video | Code | Extra Reading |
|---|---|---|---|
| ✅Model Representation | 1-2 | --- | |
| ✅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 | |
|
| ✅Understanding Gradient Descent | 1-2-3 | |
1 |
| ✅Gradient Descent For Linear Regression | 1 | |
|
| Newton Raphson method | 1 | |
| 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 | |
| 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 | |
| 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 |
| Topic Name/Tutorial | Video | Code | Extra Reading |
|---|---|---|---|
| ✅1- Decision trees | --- | --- | Link |
| ✅2- Random Forest | --- | --- | Link |
| ✅3- Gradient boosting (XGBoost) | --- | --- | Link |
| ✅4- Feature importance | --- | --- | Link |
| ✅5- Hyperparameter tuning | --- | --- | Link |
| Topic Name/Tutorial | Video | Code | Note |
|---|---|---|---|
| ✅1-Suerpvised VS unsupervised learning⭐️ | 1 | Content 3 | Doc |
| ✅2-Partition-Based Unsupervised Learning⭐️ | 1 | Content 3 | Note |
- Anomaly_Detection
- BIRCH Clustering in Machine Learning
- Anomaly_Detection_with_Isolation_Forest_algorithm
- Kmean
- Unsupervised_learning
- DBSCAN Clustering in Machine Learning
- Clus-K-Means-Customer-Seg-py-v1.ipynb
- Clus-Hierarchical-Cars-py-v1.ipynb
- Clus-DBSCN-weather-py-v1.ipynb
- Hierarchical Clustering-Agglomerative method
| Topic Name/Tutorial | Video | Code | Extra Learning |
|---|---|---|---|
| ✅1-Classification (Supervised Learning-⭐️ | 1-2-3-4 | |
|
| ✅2-Classification using Scikit-Learn⭐️ | 1 | |
| Topic Name/Tutorial | Video | Code | Extra Learning |
|---|---|---|---|
| ✅1-Regression in scikit-learn⭐️ | 1-2 | |
| Topic Name/Tutorial | Video | Code | Extra Learning |
|---|---|---|---|
| ✅-1-Preprocessing in Machine Learning-s | 1 -2-2 | ||
| ✅2- Importing the Data Set Using Scikit-Learn-s | --- | |
1 |
| ✅3-Handling missing data-S | 1 | |
|
| ✅4-Data Imbalanced problem-s | 1 | |
|
| ✅5-Data Transformation⭐️ | 1-2 | |
|
| ✅4-Centering and scaling⭐️. | 1-2-3 | |
|
| ✅5-Removing Outliers | 1-2 | |
|
| ✅6-Data Splitting⭐️ | 1-2-3-4 | |
Revisit / Update coming |
| ✅7-Pipelines in scikit-learn⭐️ | 1-2 | |
1 |
| Topic Name/Tutorial | Video | Code | Extra Learning |
|---|---|---|---|
| ✅-1-Introduction of Model Evaluation⭐️ | --- | --- | |
| ✅2- Confusion Metrix⭐️ | 1-2 | |
|
| ✅3-Accuracy⭐️ | 1 | |
|
| 🌐4-Precision-Recall-F1-score⭐️ | 1-2 | |
|
| 🌐3-Other Classification metrics⭐️ | 1-2 | |
|
| 🌐6-Understanding Regression Metrics | 1 | |
|
| 🌐7-How to Choose the Right Algorithm | --- | |
|
| 🌐8-How to Improve the Performance of Machine Learning Model | --- | |
| Topic Name/Tutorial | Video | Code | Extra Learning |
|---|---|---|---|
| 🌐1- Introduction of Hyperparameter Tuning⭐️ | 1-2-2 | |
1 |
| 🌐2- Grid Search⭐️ | 1-2-3 | |
|
| 🌐3- Random Search⭐️ | 1 | |
|
| 🌐4- Bayesian Optimization⭐️ | 1-2 | |
|
| 🌐5-Particle Swarm Optimization⭐️ | 1 | |
|
| 🌐6-Hyperopt: Distributed Hyperparameter Optimization⭐️ | 1 | |
| Topic Name/Tutorial | Video | Code | Extra Learning |
|---|---|---|---|
| 🌐1- Introduction of Feature Selection | 1 | |
|
| 🌐2-Correlation Coefficient Method | 1 | |
|
| 🌐3-Chi-Square Test Method | 1 | |
|
| 🌐4-Variance Threshold | 1 | |
|
| 🌐5-Forward Selection | 1 | |
|
| 🌐6-Backward Elimination | 1 | |
|
| 🌐7-Lasso Regression (L1 Regularization | 1 | |
|
| 🌐8-Tree-Based Feature Selection Methods | 1 | |
| Topic Name/Tutorial | Video | Code | Extra Learning | Note |
|---|---|---|---|---|
| 🌐1-Detect Overfitting and Underfitting in scikit-learn | 1-2 | |
||
| 🌐2-Learning and Validation Curves | 1-2-3 | |
1 | Link |
| 🌐3-Model Calibration: Making Probabilities Reliable⭐️ | 1 | |
Podcast | Link |
| 🌐4-Permutation Importance: Know What Really Matters⭐️ | 1 | |
Podcast | Link |
| 🌐5-FeatureHasher: Handle High-Cardinality Categorical Data⭐️ | 1 | |
Podcast | Link |
| 🌐6-RobustScaler: Handle Outliers Gracefully⭐️ | 1 | |
Podcast | Link |
| 🌐7-FeatureUnion: Combine Multiple Transformers⭐️ | 1 | |
Podcast | Link |
Deploy deep learning models using serverless technologies like AWS Lambda.
| Topic Name/Tutorial | Video | Code | Extra Reading |
|---|---|---|---|
| ✅1- Serverless concepts | --- | --- | Link |
| ✅2- Deploying Scikit-Learn models with AWS Lambda | --- | --- | Link |
| ✅3- Deploying TensorFlow and PyTorch models with AWS Lambda | --- | --- | Link |
| ✅4- API Gateway | --- | --- | Link |
Learn to serve ML models at scale using Kubernetes and TensorFlow Serving.
| Topic Name/Tutorial | Video | Code | Extra Reading |
|---|---|---|---|
| ✅1- Kubernetes basics | --- | --- | Link |
| ✅2- TensorFlow Serving | --- | --- | Link |
| ✅3- Model deployment and scaling | --- | --- | Link |
| ✅4- Load balancing | --- | --- | Link |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-How to Deploy an AI App Locally: Step-by-Step Guide for Beginners) | --- | |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-How to Deploy an AI App Locally: Step-by-Step Guide for Beginners) | --- | |
- Bagging_&_Random_Forests
- Reg-Mulitple-Linear-Regression-Co2-py-v1.ipynb
- KNN with Python
- Build Machine Learning Pipelines
- Simple_Linear_Regression_using_scikit_learn
- Linear_Regression_Andrew
- Supervised_(Classification)_ML_Model_Training_and_Evulation
- Reg-NoneLinearRegression-py-v1.ipynb
- Reg-Polynomial-Regression-Co2-py-v1.ipynb
- Reg-Simple-Linear-Regression-Co2-py-v1.ipynb
- Clas-Decision-Trees-drug-py-v1.ipynb
- Clas-K-Nearest-neighbors-CustCat-py-v1.ipynb
- Voting_Classifiers.ipynb
- Perceptron in Machine Learning
- Decision_Trees
- Linear_Regression
- XGBoost_in_Machine_Learning.ipynb
- Model_Evaluation_&_Scoring_Matrices
- Naive Bayes Algorithm in Machine Learning
- Naive_Bayes
- Nerual Networks
- Supervised_learning_with_Sklearn
- PyCaret in Machine Learning
Module 03 - Preprocessing with scikit_learn
- Data_Processing_in_Python_.ipynb
- Upload_Dataset_from_github_to_Colab.ipynb
- Feature_Selection
- Create_new_Features_(Faker)
- Give_Columns_name_to_dataset_(resize)_using_Python
- StandardScaler in Machine Learning
- Creating_artificial_datasets.ipynb
- Data_representation_in_scikit_learn.ipynb
Module 04 - Anomaly Detection
Module -Recommendation System
Module 04 - Model Evaluation with scikit_learn
- Bias and Variance using Python
- hyperparameter_tuning.ipynb
- What_is_Cross_Validation_in_Machine_Learning_.ipynb
- Scikit_Plot_Visualizing_Machine_Learning_Algorithm_Results_&_Performance (1).ipynb
Module 06 - Statistics
Module 07 - Machine Learning with Pycaret
- 50 Machine Learning Algorithms Explained using Python
- Akramz / Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow Public
- Data Cleaning with Python
- 70+ Machine Learning Algorithms & Models Explained with Python
- Interpreting Tree-Based Model's Prediction of Individual Sample
- Predicting presence of Heart Diseases using Machine Learning
- How to Master Scikit-learn for Data Science
- All Machine Learning Algorithms & Models Explained
- Python AI: How to Build a Neural Network & Make Predictions
- 60 Machine Learning Algorithms & Models Explained with Python
- ageron/handson-ml2
- All Machine Learning Algorithms & Models with Python
- How to Master Scikit-learn for Data Science
- rushter/MLAlgorithms
- 80+ Machine Learning Algorithms & Models Explained with Python
- 5x12themlsbook
- edyoda data-science-complete-tutorial
- ageron handson-ml Public
| No. | Title/Link | Description | Reading Status | University / Platform | Feedback |
|---|---|---|---|---|---|
| 1 | Machine Learning Specialization | By Andrew Ng, Coursera | In Progress | Coursera | ⭐️⭐️⭐️⭐️ |
| 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 | ⭐️⭐️⭐️ | |
| 20 | Introduction to Algorithms, Spring 2020 by Youtube | Complete sets of Lecture Slides and Videos | Youtub | ⭐️⭐️⭐️ | |
| 21 | OpenAI Academy AI Courses | OpenAI Academy AI Courses | OpenAI | ⭐️⭐️⭐️ | |
| 22 | Hugging Face AI Courses | Complete sets of Lecture Slides and Videos | Youtub | ⭐️⭐️⭐️ | |
| 23 | LangChain Academy AI Course | Complete sets of Lecture Slides and Videos | Youtub | ⭐️⭐️⭐️ | |
| 24 | IBM's AI courses | Complete sets of Lecture Slides and Videos | Youtub | ⭐️⭐️⭐️ | |
| 25 | QPT's AI Course | Complete sets of Lecture Slides and Videos | Youtub | ⭐️⭐️⭐️ | |
| 26 | AI For Beginners by Microsoft | Complete sets of Lecture Slides and Videos | Youtub | ⭐️⭐️⭐️ | |
| 27 | Introduction to Robotics | Complete sets of Lecture Slides and Videos | Youtub | ⭐️⭐️⭐️ | |
| 28 | Notes for: Machine Learning in Computational Biology, Fall 2024 | Complete sets of Lecture Slides and Videos | Youtub | ⭐️⭐️⭐️ | |
| 28 | CORNELL CS4780 "Machine Learning by Kilian Weinberger | 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 |
| ✅ 6-Machine Learning Visualized | Visualizes Machine Learning Algorithms. | InProgress |
| ✅ 7-scikit-learn-mooc | Visualizes Machine Learning Algorithms. | InProgress |
| ✅ 8-Machine Learning Resources | Visualizes Machine Learning Algorithms. | InProgress |
| ✅ 8-Made With ML | Visualizes Machine Learning Algorithms. | InProgress |
This table provides an overview of essential machine learning algorithms, organized by learning paradigm (Supervised, Unsupervised, Semi-Supervised, Reinforcement, Deep Learning), with their key strengths, main limitations, and common use cases.
| # | Name | Purpose / Description | Strengths | Category | Sub-Category | Use Cases | Main Limitation / Note |
|---|---|---|---|---|---|---|---|
| 1 | Naïve Bayes | Probabilistic classifier | Simple, fast, works with small data | Supervised | Classification | Spam filtering, sentiment analysis | Assumes feature independence |
| 2 | Logistic Regression | Binary/Multiclass classification | Interpretable, efficient for linearly separable data | Supervised | Classification | Medical diagnosis, credit scoring | Not effective for non-linear data |
| 3 | Neural Network (MLP) | Complex pattern recognition, function approximation | Handles non-linearities, scalable | Supervised | Classification/Regression | Image recognition, speech-to-text | Requires large data, less interpretable |
| 4 | K-Nearest Neighbor (KNN) | Instance-based learning | Non-parametric, easy to implement | Supervised | Classification/Regression | Pattern recognition, recommendation systems | Sensitive to outliers, slow on large data |
| 5 | Gradient Boosting Machine (GBM) | Ensemble boosting method | High accuracy, handles mixed data | Supervised | Classification/Regression | Fraud detection, churn prediction | Prone to overfitting, slower to train |
| 6 | Random Forest | Ensemble of decision trees | High accuracy, handles non-linearity | Supervised | Classification/Regression | Fraud detection, healthcare analytics | Less interpretable than single trees |
| 7 | Support Vector Machine (SVM) | Classification & regression | Effective in high-dimensional spaces | Supervised | Classification/Regression | Text categorization, bioinformatics | Not efficient with large datasets |
| 8 | Decision Tree | Predictive modeling | Easy to interpret, non-linear boundaries | Supervised | Classification/Regression | Risk analysis, customer segmentation | Prone to overfitting |
| 9 | Simple Linear Regression | Predict continuous values | Easy to interpret, fast | Supervised | Regression | Sales prediction, trend analysis | Assumes linearity, sensitive to outliers |
| 10 | Multivariate Regression | Multiple input variables | Handles multiple predictors | Supervised | Regression | Forecasting, demand estimation | Complex, needs more data |
| 11 | Lasso Regression | Regression with feature selection | Reduces overfitting, variable selection | Supervised | Regression | Feature selection, sparse modeling | Can eliminate useful variables |
| 12 | CatBoost | Gradient boosting with categorical support | Handles categorical features natively | Supervised | Classification/Regression | Credit scoring, e-commerce analytics | Proprietary, slower than LightGBM |
| 13 | LightGBM | Fast gradient boosting | Very fast, low memory usage | Supervised | Classification/Regression | Ranking, click prediction | Sensitive to overfitting, less interpretable |
| # | Name | Purpose / Description | Strengths | Category | Sub-Category | Use Cases | Main Limitation / Note |
|---|---|---|---|---|---|---|---|
| 14 | K-Means Clustering | Group similar data points | Scalable, simple, widely used | Unsupervised | Clustering | Market segmentation, image compression | Needs number of clusters, sensitive to scale |
| 15 | DBSCAN Algorithm | Density-based clustering | Finds clusters of arbitrary shape, noise resistant | Unsupervised | Clustering | Anomaly detection, spatial data | Struggles with varying densities |
| 16 | Principal Component Analysis (PCA) | Dimensionality reduction | Reduces complexity, removes redundancy | Unsupervised | Dimensionality Reduction | Image compression, feature extraction | Assumes linearity, loses interpretability |
| 17 | Independent Component Analysis (ICA) | Signal separation | Extracts independent signals from mixtures | Unsupervised | Dimensionality Reduction | EEG analysis, blind source separation | Sensitive to noise |
| 18 | Agglomerative Clustering | Hierarchical grouping of data | No need to pre-specify clusters, dendrogram visualization | Unsupervised | Clustering | Customer segmentation, document clustering | Computationally expensive |
| 19 | Frequent Pattern Growth | Mining frequent itemsets | Efficient association rule mining | Unsupervised | Association | Market basket analysis, recommender systems | Needs large data, complex output |
| 20 | Apriori Algorithm | Rule-based learning | Classic association mining, easy to understand | Unsupervised | Association | Market basket analysis, cross-selling | Slow on large datasets |
| 21 | Z-Score Algorithm | Detect outliers | Simple statistical method | Unsupervised | Anomaly Detection | Fraud detection, quality control | Assumes normal distribution |
| 22 | Isolation Forest Algorithm | Tree-based anomaly detection | Scales well to large datasets | Unsupervised | Anomaly Detection | Intrusion detection, anomaly monitoring | Less interpretable |
| # | Name | Purpose / Description | Strengths | Category | Sub-Category | Use Cases | Main Limitation / Note |
|---|---|---|---|---|---|---|---|
| 23 | Self-Training | Semi-supervised classification | Uses unlabeled data to improve accuracy | Semi-Supervised | Classification | NLP tasks, medical data with limited labels | May reinforce errors |
| 24 | Co-Training | Semi-supervised regression | Works with multiple views of data | Semi-Supervised | Regression | Text classification, weakly labeled datasets | Needs multiple independent feature sets |
| # | Name | Purpose / Description | Strengths | Category | Sub-Category | Use Cases | Main Limitation / Note |
|---|---|---|---|---|---|---|---|
| 25 | Policy Optimization | Learn optimal actions | Improves agent strategies | Reinforcement | Model-Free | Robotics, gaming | Training can be unstable |
| 26 | Q-Learning | Value-based learning | Off-policy, model-free RL | Reinforcement | Model-Free | Game AI, recommendation systems | Needs large training time |
| 27 | Learn the Model | Model-based planning | Learns transition dynamics | Reinforcement | Model-Based | Robotics, simulations | Requires accurate environment model |
| 28 | Given the Model | Uses known environment | Faster training with known rules | Reinforcement | Model-Based | Chess AI, dynamic programming | Limited to environments with known models |
| # | Name | Purpose / Description | Strengths | Category | Sub-Category | Use Cases | Main Limitation / Note |
|---|---|---|---|---|---|---|---|
| 29 | Convolutional Neural Network (CNN) | Image/video processing & recognition | Captures spatial features, excellent for images | Deep Learning | Supervised/Unsupervised | Image classification, object detection | Needs large data, computationally heavy |
| 30 | Recurrent Neural Network (RNN), LSTM, GRU | Sequence modeling, time series | Remembers temporal information | Deep Learning | Supervised/Unsupervised | Speech recognition, time series | Vanishing gradients, slow training |
| 31 | Transformer (Attention) Models | Sequence-to-sequence, context-rich learning | Handles long data dependencies, parallelizable | Deep Learning | Supervised/Unsupervised | NLP, translation, summarization | Data hungry, requires large compute |
| 32 | BERT, RoBERTa, DistilBERT | Pre-trained language representations | Powerful for transfer learning in NLP | Foundation Model | NLP | Text classification, Q&A, embeddings | Large models, inference cost |
| 33 | GPT, LLaMA, Falcon, Mistral | Generative language models | Few-shot, zero-shot, strong generative ability | Foundation Model | NLP | Text generation, code completion | Very large, costly to train/infer |
| 34 | Vision Transformer (ViT) | Image understanding with transformer | Handles large-scale images, robust | Foundation Model | Vision | Image classification, segmentation | Needs huge data, slow to train |
| 35 | Stable Diffusion, DALL-E | Text-to-image synthesis | Generates realistic images from text prompts | Generative Model | Vision | Art generation, design, creative apps | May produce unrealistic images |
| 36 | CLIP, BLIP, Flamingo | Cross-modal (vision+language) representation | Links text and images, multi-modal learning | Foundation Model | Multi-modal | Image search, captioning | Needs large pre-training datasets |
| Title | Description | Status |
|---|---|---|
| ✅ 1-ProjectPro Podcast | Comprehensive roadmap for AI courses | Completed |
| Title | Description | Status |
|---|---|---|
| ✅ 1-Machine-Learning-with-Python | Comprehensive roadmap for AI courses | Completed |
| ✅ 2-Machine Learning Notebooks | Comprehensive roadmap for AI courses | Completed |
| ✅ 3-Machine Learning with Python: Concepts and Applications | Comprehensive roadmap for AI courses | Completed |
| 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 |
| ✅ 5- Scikit-learn Cheatsheet | YouTube channel focused on teaching Scikit-learn and data science | Pending | Github |
| 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 |
| ✅ 8- Awesome-ai-ml-resources | This repository contains free resources and a roadmap to learn Machine Learning and Artificial Intelligence in 2025. | Pending |
| ✅ 9- best-of-ml-python | This curated list contains 920 awesome open-source projects with a total of 5M stars grouped into 34 categories.. | Pending |
| ✅ 10- Awesome Production Machine Learning | This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning | Pending |
| ✅ 11- Machine-Learning-with-Python | This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning | Pending |
| ✅ 12-Hyperparameter Optimization of Machine Learning Algorithms | This code provides a hyper-parameter optimization implementation for machine learning algorithms, as described in the paper | Pending |
| Title | Description | Resources |
|---|---|---|
| 🌐1- scikit-learn | Simple machine learning in Python for classification, regression, and clustering | --- |
| 🌐2- Pandas | Easy data manipulation and analysis with DataFrames | --- |
| 🌐3- NumPy | Fundamental array computing and linear algebra operations | --- |
This diagram illustrates the complete end-to-end process followed in this project, from raw data preprocessing to model deployment.
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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!🚀