Welcome to the A-Z Guide to Machine Learning repository! This comprehensive supplement offers a thorough exploration of the world of Machine Learning, providing implementation examples of various ML algorithms and techniques in Python and other relevant languages.
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.
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.
If you want to contact me, you can reach me through social handles.
🙏 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 |
|---|---|---|
| 🌐1- Introduction to Artificial Intelligence (AI)⭐️ | 1-2-2 | Content 3 |
| 🌐2- What is machine learning?⭐️ | 1-2-3-4-5 | -6-7 |
| 🌐3-Types of Machine Learning?⭐️ | 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 |
|---|---|---|
| 🌐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 | |
| 🌐What the cost function is doing?⭐️ | 1 | |
| 🌐Understanding Gradient Descent⭐️ | 1-2-3 | |
| 🌐Gradient Descent For Linear Regression⭐️ | 1 | |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐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 |
|---|---|---|
| 🌐1-Cost Function⭐️ | 1 | |
| 🌐2-Backpropagation⭐️ | 1 | |
| 🌐3-Backpropagation intuition⭐️ | 1-2 | |
| 🌐4-Implementation Note - Unrolling Parameters⭐️ | 1 | |
- 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 |
|---|---|---|
| 🌐1-Classification (Supervised Learning-⭐️ | 1234 | |
| 🌐2-Classification using Scikit-Learn⭐️ | 1 | |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1-Regression in scikit-learn⭐️ | 1-2 | |
| Topic Name/Tutorial | Video | Code |
|---|---|---|
| 🌐1- Introduction of Hyperparameter Tuning⭐️ | 1-2-2 | |
| 🌐2- Grid Search⭐️ | 1-2-3 | |
| 🌐3- Random Search⭐️ | 1 | |
| 🌐4- Bayesian Optimization⭐️ | 1 | |
| 🌐5-Particle Swarm Optimization⭐️ | 1 | |
| 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
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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.
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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!”
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Thanks goes to these Wonderful People. Contributions of any kind are welcome!🚀