Applied Machine Learning – Course Projects

This repository contains a collection of hands-on projects and assignments completed as part of an Applied Machine Learning (AML) course. The projects demonstrate practical implementation of machine learning concepts, end-to-end ML workflows, and model evaluation using Python and Jupyter Notebooks.

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📌 Topics Covered

• Data preprocessing and exploratory data analysis (EDA)
• Feature engineering and data transformation
• Supervised learning (regression and classification)
• Unsupervised learning (clustering)
• Model evaluation and performance metrics
• Visualization and result interpretation

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🛠 Technologies & Tools

• Programming Language: Python
• Libraries: scikit-learn, pandas, NumPy, matplotlib
• Environment: Jupyter Notebook

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📂 Project Structure

Each notebook in this repository corresponds to an individual AML assignment or mini-project.
The notebooks follow a structured approach:

1. Problem understanding
2. Data exploration and preprocessing
3. Model selection and training
4. Evaluation and analysis of results

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🎯 Learning Outcomes

Through these projects, I gained hands-on experience in:

• Implementing machine learning algorithms on real-world datasets
• Designing reproducible ML pipelines
• Evaluating and comparing models using appropriate metrics
• Translating problem statements into data-driven solutions