🧠 Machine Learning Specialization – DeepLearning.AI & Stanford University

This repository contains my solutions to the assignments from the Machine Learning Specialization by DeepLearning.AI and Stanford University, taught on Coursera.

Instructors: Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig

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📚 Courses Included

📘 Course 1: Supervised Machine Learning – Regression and Classification

In this course, you will:

• Build machine learning models in Python using NumPy and scikit-learn
• Train models for prediction and binary classification, including linear regression and logistic regression

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📙 Course 2: Advanced Learning Algorithms

In this course, you will:

• Build and train neural networks using TensorFlow
• Apply ML development best practices to ensure models generalize well
• Build and use decision trees and ensemble methods (random forests, boosted trees)

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📗 Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning

In this course, you will:

• Use clustering and anomaly detection techniques
• Build recommender systems with collaborative filtering and deep learning
• Develop reinforcement learning models

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🔗 Course Link

Machine Learning Specialization on Coursera

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📝 Disclaimer

This repository is created for educational and reference purposes only.
Please do not copy and paste the solutions as-is into Coursera. You’ll learn much more by following the instructions and solving the assignments yourself.

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📄 License

This project is licensed under the MIT License.

This repository contains personal solutions to publicly available assignments. All course materials belong to DeepLearning.AI and Coursera.