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Machine Learning Tutorials

Conda Python R

Tests pages-build-deployment

📘 Introduction

Welcome! This repository is a personal project designed to demonstrate machine learning techniques and explore tools from the data science and software development ecosystem.

The code and notebooks are structured as hands-on tutorials, so feel free to follow along, experiment, and build on them.

To get started, please set up your virtual environment. Instructions can be found on the Requirements page.

🤖 Supervised Learning

Supervised learning is a machine learning approach where a model is trained on labeled data—that is, input-output pairs. The goal is to learn a general rule that maps inputs to outputs. This type of learning is a form of predictive modelling, where models are trained on historical data to make predictions about new, unseen data.

🔍 Examples of Supervised Learning

  • Forecasting energy consumption
    A model is trained on a dataset containing daily energy usage over the course of a year. It then predicts energy usage for future days based on patterns it has learned.
    → This is an example of regression, because the target output (energy usage) is a continuous variable.

  • Email spam detection
    A model is trained on a labeled dataset of emails, where each email is marked as spam or not spam. The model learns to classify new, unseen emails accordingly.
    → This is an example of classification, because the target output is a categorical variable (e.g., {"spam", "not spam"} or {0, 1}).

📈 Regression

Regression analysis refers to a family of methods used to estimate the relationship between a continuous dependent variable (also called the output, response, or label) and one or more independent variables (also called features, predictors, or regressors).

🔍 Regression Examples

🧮 Classification

Classification is the task of predicting categorical labels from input data. The model learns to assign new data points to one of a finite set of classes.

🔍 Classification Examples