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It contains notes, code implementations, and projects from the 3-course series.\n\n\u003cdiv align=\"center\"\u003e\n\n![Machine Learning Specialization](./Images/ml-specialization.png)\n\n\u003c/div\u003e\n\n## About the Specialization\n\nThe 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.\n\n## Courses in the Specialization\n\n### `Course 1|` Supervised Machine Learning: Regression and Classification\n\nLearn the fundamentals of predictions, regression, and classification tasks. There are 3 modules in this course:\n\n- `Week 1|` Introduction to Machine Learning\n- `Week 2|` Regression with multiple input variables\n- `Week 3|` Classification\n\n**Earned Certificate**: [Supervised Machine Learning: Regression and Classification](https://coursera.org/share/789489cb15a016ca08d5d4377bca23af)\n\n### `Course 2|` Advanced Learning Algorithms\n\nExplore decision trees, ensemble methods, and neural network training. There are 4 modules in this course:\n\n- `Week 1|` Neural Networks\n- `Week 2|` Neural network training\n- `Week 3|` Advice for applying machine learning\n- `Week 4|` Decision trees\n\n**Earned Certificate**: [Advanced Learning Algorithms](https://coursera.org/share/a5be0e2325dc9e4c0dfab17d30719635)\n\n### `Course 3|` Unsupervised Learning, Recommenders, Reinforcement Learning\n\nLearn how to work with data without labels, personalize recommendations, and build agents that learn from interactions. There are 3 modules in this course:\n\n- `Week 1|` Unsupervised learning\n- `Week 2|` Recommender systems\n- `Week 3|` Reinforcement learning\n\n**Earned Certificate**: [Unsupervised Learning, Recommenders, Reinforcement Learning](https://coursera.org/share/71aa2bcf08739a91cd90e67c57c973dc)\n\n## Learning Goals\n\nBy completing this specialization, I aim to:\n\n- Build machine learning models in Python using libraries like NumPy and scikit-learn.\n- Train and evaluate supervised models such as linear regression, logistic regression, and neural networks.\n- Implement unsupervised learning techniques like clustering and dimensionality reduction.\n- Build recommender systems and deep reinforcement learning models.\n- Apply best practices for model evaluation and improvement.\n\n## Repository Contents\n\n- **Notes**: Summaries of concepts, techniques, and formulas covered in the courses.\n- **Projects**: Hands-on projects implementing ML models and algorithms.\n- **Code**: Python scripts and Jupyter notebooks for course exercises.\n\n## Technologies Used\n\n- **Programming Language**: Python\n- **Libraries**: NumPy, Matplotlib, Scikit-learn, TensorFlow\n- **Tools**: Jupyter Notebook, Git\n\n## Acknowledgements\n\nThis specialization is taught by **Andrew Ng**, a renowned AI researcher and educator, and is developed in partnership with DeepLearning.AI and Stanford Online.\n\nFeel free to explore, learn, and contribute!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchoaib-elmadi%2Fmachine-learning-specialization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchoaib-elmadi%2Fmachine-learning-specialization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchoaib-elmadi%2Fmachine-learning-specialization/lists"}