Introduction to Python and Machine Learning

This repository hosts scripts from a tutorial series I led for corporate training, designed to enhance technical skills within the company.

Gaussian Naive Bayes:

Explaining briefly the math behind Naive Bayes classification and a plant classification example using Gaussian distribution.

K Nearest Neighbor:

Visualizing the idea behind KNN and giving a hands-on example using banking data.

Logistic Regression:

Explaining briefly the math behind logistic regression and giving a hands-on example using banking data.

Multiple Linear Regression and Regularization:

Explaining the math behind linear regression and multiple linear regression, giving an example using stock market data. Explaining regularization and illustrating Ridge and Lasso regularizations' effect on regression.

Artificial Neural Networks:

Explaining the architecture and potential loss functions used in neural network training and training a simple ANN model for plant classification using PyTorch.