🧠 Machine Learning Specialization – Practice & Projects

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📌 Overview

This repository contains my implementations, notes, and projects while completing the Machine Learning Specialization (DeepLearning.AI / Coursera).

It covers the three courses in the specialization:

• Course 1: Supervised Machine Learning (Regression & Classification)
• Course 2: Advanced Learning Algorithms
• Course 3: Unsupervised Learning, Recommenders, and Reinforcement Learning

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🧭 Repository Structure

01_supervised_learning/
02_advanced_learning_algorithms/
03_unsupervised_learning/
assets/

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🚀 Featured Project

🖼️ MNIST Image Classifier (Softmax Neural Network)

Located in:

02_advanced_learning_algorithms/image_classifier_mnist/

🔍 What it does:

• Classifies handwritten digits (0–9)
• Uses a neural network with softmax output
• Outputs probability distribution per class

🧠 Concepts used:

• Softmax function
• Cross-entropy loss
• Neural networks
• Argmax classification

📊 Example Output:

• Test accuracy: ~97–98%
• Probabilistic predictions per image

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🧪 Tech Stack

• Python 🐍
• TensorFlow / Keras
• NumPy
• Matplotlib
• Jupyter Notebooks

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🎯 Learning Goals

• Develop intuition for core machine learning algorithms
• Understand neural network architectures and training dynamics
• Gain practical experience with TensorFlow/Keras
• Learn to structure ML projects in a professional way

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🔮 Future Work

• Add more visualizations for model performance
• Implement additional ML algorithms from the specialization
• Build interactive demos (e.g., digit drawing classifier)
• Explore hyperparameter tuning experiments

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⚙️ Installation

git clone https://github.com/your-username/machine-learning-specialization.git
cd machine-learning-specialization
pip install -r requirements.txt

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📦 Requirements

Create requirements.txt:

tensorflow
numpy
matplotlib
scikit-learn

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📌 Status

🚧 Currently in progress as part of ML Specialization coursework.

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📜 License

This repository is open for learning purposes.

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⭐ Author

Mete Kaba Machine Learning learner | Building from fundamentals to deep learning systems