A-Z Guide to Machine Learning👋🛒

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.

Overview👋🛒

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.

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.

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.

📬 Contact

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

Star this repo if you find it useful ⭐

Also please subscribe to my youtube channel!

Course 01 - ⚙️Machine Learning

📚Chapter: 1 - Introduction

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⭐️	---	---

📚Chapter: 2 -Linear Regression with one Variable

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

📚Chapter: 3 -Linear Algebra

Topic Name/Tutorial	Video	Code
🌐1-Understanding Matrices and Vectors in Linear Algebra	1
🌐2-Understanding Addition and Scalar Multiplication of Matrices	1
🌐3-Matrix-Vector Multiplication	1
🌐4-Matrix-Matrix Multiplication	1
🌐5-Matrix multiplication Properties	1
🌐6-Inverse and Transpose	1

📚Chapter: 4 -Linear Regression with Multiple Variable

Topic Name/Tutorial	Video	Code
🌐1-Multiple Features(multivariate linear regression)	1
🌐2-Gradient Descent for Multiple Variables	1
🌐3-Gradient Descent in Practice I — Feature Scaling	1
🌐4-Gradient Descent in Practice II — Learning Rate	1
🌐5-Features and Polynomial Regression	1
🌐6-Normal Equation	1

📚Chapter: 5 -Logistic Regression

Topic Name/Tutorial	Video	Code
🌐1-Classification	1
🌐2-Hypothesis Representation of Logistic Regression	1
🌐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
🌐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-linear Hypotheses	1
🌐2-The Science Behind Neural Networks: Exploring	1
🌐3- Model Representation 2	1
🌐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
🌐1-Cost Function⭐️	1

Course 02 - 📚Unsupervised Learning with scikit_learning

Course 02 -📚🧑‍🎓Unsupervised Learning with scikit_learn

• 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

Course 03 - 📚Supervised Learning with scikit_learn

📚Chapter:1-Classification

Topic Name/Tutorial	Video	Code
🌐1-Classification (Supervised Learning-⭐️	1234
🌐2-Classification using Scikit-Learn⭐️	1

📚Chapter:2-Regression

Topic Name/Tutorial	Video	Code
🌐1-Regression in scikit-learn⭐️	1-2

📚Chapter:3-Data Preprocessing and Pipelines

Topic Name/Tutorial	Video	Code
🌐-1-Preprocessing in Machine Learning⭐️	1 -2-2
🌐2- Importing the Data Set Using Scikit-Learn	---
🌐3-Handling missing data	1
🌐4-Data Imbalanced problem	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
🌐7-Pipelines in scikit-learn	1-2

📚Chapter:4-Measuring model performance

Topic Name/Tutorial	Video	Code
🌐-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
🌐6-Understanding Regression Metrics	1
🌐7-How to Choose the Right Algorithm	---
🌐8-How to Improve the Performance of Machine Learning Model	---

📚Chapter:5-Fine Tuning your model

Topic Name/Tutorial	Video	Code
🌐1- Introduction of Hyperparameter Tuning⭐️	1-2
🌐2- Grid Search⭐️	1

Course 04 - 📚Machine Learning in Production

📚Chapter:3 -Apps Deployment

Topic Name/Tutorial	Video	Code
🌐1-How to Deploy an AI App Locally: Step-by-Step Guide for Beginners)	---

Course 01 - 🗞️📚Other Best Free Resources to Learn Machine learning

• 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

• Association Mining
  • Apriori_Algorithm

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

• Anomaly_Detection.ipynb
• Anomaly_Detection_using_Using_Python library.ipynb

Module -Recommendation System

• Collaborative-Filtering
• Content-Based

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 05 - Data Visualization and Exploratoration with scikit_learn

• Automate_Exploratory_Data_Analysis
• Data_Exploratory_and_Ploting

Module 06 - Statistics

• Statistics_for_Machine_Learning

Module 07 - Machine Learning with Pycaret

• Classification
• Regression
• Clustering
• Anomaly Detection

• Deep learning library
  • FastAI_in_Machine_Learning.ipynb

Module 07 - [Distance Measure ]

• Distance Measure

Module 06 - Model Need to implement

• 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

💻 Workflow:

• Fork the repository

• Clone your forked repository using terminal or gitbash.

• Make changes to the cloned repository

• Add, Commit and Push

• Then in Github, in your cloned repository find the option to make a pull request

print("Start contributing for Machine Learning")

⚙️ Things to Note

• 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!”

• Supervised learning with scikit-learn
• Fundamental of Machine Learning

✨Top Contributors

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!🚀