https://github.com/choaib-elmadi/machine-learning-specialization

A repository documenting my journey through the Machine Learning Specialization on Coursera, offered by DeepLearning.AI and Stanford Online. It contains notes, code implementations, and practical applications.
https://github.com/choaib-elmadi/machine-learning-specialization

ai artificial-intelligence classification coursera deep-learning machine-learning machine-learning-algorithms ml neural-networks regression regression-models

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A repository documenting my journey through the Machine Learning Specialization on Coursera, offered by DeepLearning.AI and Stanford Online. It contains notes, code implementations, and practical applications.

• Host: GitHub
• URL: https://github.com/choaib-elmadi/machine-learning-specialization
• Owner: Choaib-ELMADI
• Created: 2024-11-18T09:04:26.000Z (2 months ago)
• Default Branch: master
• Last Pushed: 2024-12-17T20:57:28.000Z (about 1 month ago)
• Last Synced: 2024-12-17T21:35:40.472Z (about 1 month ago)
• Topics: ai, artificial-intelligence, classification, coursera, deep-learning, machine-learning, machine-learning-algorithms, ml, neural-networks, regression, regression-models
• Language: Jupyter Notebook
• Homepage:
• Size: 7.73 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# Machine Learning Specialization

This repository showcases my journey through the `Machine Learning Specialization` on Coursera, offered by `DeepLearning.AI` in collaboration with `Stanford Online`. It contains notes, code implementations, and projects from the 3-course series.



![Machine Learning Specialization](./Images/ml-specialization.png)



## About the Specialization

The Machine Learning Specialization is an updated and expanded version of Andrew Ng's pioneering Machine Learning course. Taught by **Andrew Ng**, this beginner-friendly program provides a comprehensive introduction to modern machine learning. It covers supervised learning, unsupervised learning, and key best practices used in the field.

## Courses in the Specialization

### `Course 1|` Supervised Machine Learning: Regression and Classification

Learn the fundamentals of predictions, regression, and classification tasks. There are 3 modules in this course:

- `Week 1|` Introduction to Machine Learning

- `Week 2|` Regression with multiple input variables

- `Week 3|` Classification

**Earned Certificate**: [Supervised Machine Learning: Regression and Classification](https://coursera.org/share/789489cb15a016ca08d5d4377bca23af)

### `Course 2|` Advanced Learning Algorithms

Explore decision trees, ensemble methods, and neural network training. There are 4 modules in this course:

- `Week 1|` Neural Networks

- `Week 2|` Neural network training

- `Week 3|` Advice for applying machine learning

- `Week 4|` Decision trees

### `Course 3|` Unsupervised Learning, Recommenders, Reinforcement Learning

Dive into clustering, anomaly detection, recommender systems, and deep reinforcement learning.

## Learning Goals

By completing this specialization, I aim to:

- Build machine learning models in Python using libraries like NumPy and scikit-learn.

- Train and evaluate supervised models such as linear regression, logistic regression, and neural networks.

- Implement unsupervised learning techniques like clustering and dimensionality reduction.

- Build recommender systems and deep reinforcement learning models.

- Apply best practices for model evaluation and improvement.

## Repository Contents

- **Notes**: Summaries of concepts, techniques, and formulas covered in the courses.

- **Projects**: Hands-on projects implementing ML models and algorithms.

- **Code**: Python scripts and Jupyter notebooks for course exercises.

## Technologies Used

- **Programming Language**: Python

- **Libraries**: NumPy, Matplotlib, Scikit-learn

- **Tools**: Jupyter Notebook, Git

## Acknowledgements

This specialization is taught by **Andrew Ng**, a renowned AI researcher and educator, and is developed in partnership with DeepLearning.AI and Stanford Online.

Feel free to explore, learn, and contribute!