Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Small machine learning experiments

A brief notebook on Influence Function (IF) for classical generative models (e.g., k-NN, KDE, GMM)

My Jupyter Notebooks created while learning to train algorithms

Implementation notebooks and scripts of Supervised Learning Algorithms from scratch.

Mediante este notebook se realiza un análisis predictivo de hospitalizaciones utilizando algoritmos de clasificación.

Wise recommendation for students; community for students have similar interests to communicate with. 📓

This repository contains an academic project developed in jupyter notebook using python language and machine learning algorithms.

This repository contains a Jupyter notebook that implements and optimizes several machine learning models on a dataset

K-Nearest neighbours predictions on a classified data. Data set from Kaggle. Requires IPython Notebooks to be run ;)

This notebook loads a time series of gas concentrations in the air to train a recurrent neural network to predict the next hour of data

A collection of python notebooks containing unsupervised ML algorithms, supervised ML algorithms, and regression models I wrote as part of my research project on flood prediction.

Project made in Jupyter Notebook with Kaggle Credit Card Fraud Detection Dataset 2023, which aims at selection of best supervised machine learning model for capturing credit card frauds.

This Jupyter Notebook demonstrates the implementation of a K-Nearest Neighbors (KNN) algorithm using the concept of nearest neighbors without using direct classifiers. It also includes exploratory data analysis (EDA) and comparison of three classifiers.

This notebook is about creating a 2D dataset and using supervised machine learning algorithms like K-Nearest Neighbor, Support Vector Machine and Linear Regression to classify data points then selecting the best parameters using cross validation method, and finally comparing the results.

Explore the world of classification with this K-Nearest Neighbors (KNN) model implementation on the well-known Iris dataset. Leveraging Python and Jupyter Notebook, the repository provides a step-by-step guide to understanding the model, evaluating its performance, and visualizing the results.

We use a tabular dataset which contains health information of patients to predict whether they suffer from a heart disease. Two notebooks are present currently in the repo, one focuses on data preprocessing, exploration and visualisation, while the other focuses on model creation, training and evaluation.

This repository contains a collection of lab tasks, assignments, and projects designed to learn and practice key concepts in Machine Learning. It includes hands-on Jupyter notebooks covering fundamental ML techniques, real-world projects, and theoretical exercises. Ideal for students and enthusiasts aiming to deepen their understanding of ML.

Jupyter Notebooks exploring Machine Learning techniques -- regression, classification (K-nearest neighbour (KNN), Decision Trees, Logistic regression vs Linear regression, Support Vector Machine), clustering (k-means, Hierarchical Clustering, DBSCAN), sci-kit learn and SciPy -- and where it applies to the real world, including cancer detection, …