ML-with-scikit-learn

A collection of machine learning exercises completed during my ML course at the Technical University of Munich (TUM). This repository contains hands-on implementations of various machine learning concepts, covering regression, classification, clustering and more. Each exercise is structured as a Jupyter Notebook, complete with explanations, visualizations, and reproducible code.

📌 Topics Covered:

✔️ Linear & Polynomial Regression, Regularization (Ridge, Lasso)
✔️ Logistic Regression
✔️ k-NN
✔️ SVM
✔️ Tree based methods - AdaBoost, Gradient Boosting...
✔️ XGBoost
✔️ Naive Bayes
✔️ Dimensionality reduction - PCA, LDA
✔️ Clustering - KMeans, Spectral Clustering, GMM

🚀 Getting Started

1. Clone this repository:

   git clone https://github.com/AidasBat/ML-with-scikit-learn.git

2. Install dependencies

   pip install -r requirements.txt

3. Open Jupyter Notebook

   jupyter notebook