Machine-Learning-Labs

Hands-on machine learning labs in Python (Jupyter Notebook) covering many ML algorithms and techniques including logistic regression, Naïve Bayes, decision trees, ensemble learning, SVM, neural networks, regularization, and feature selection.

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📁 Structure

Lab	Topic	Methods
Lab01/	Logistic Regression	Linear, Binary, Multi-class, Multi-label LR
Lab02/	Naïve Bayes	Multinomial NB, Gaussian NB
Lab03/	Decision Trees & Ensemble Learning	DTs, Bagging, Boosting
Lab04/	SVM & Optimization	SVM (primal & dual), SGD, SMO
Lab05/	Neural Networks	Backpropagation, Activation functions, Optimizers
Lab06/	Regularization & Feature Selection	L1/L2 regularization, Feature selection methods

Each lab folder contains a Jupyter Notebook (.ipynb) and its own dataset.

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🛠️ Requirements

pip install numpy pandas matplotlib scikit-learn jupyter

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🚀 Getting Started

git clone https://github.com/your-username/ml-labs.git
cd ml-labs
jupyter notebook

Then open the desired lab folder and run the notebook.

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