Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

This repository contains introductory notebooks for principal component analysis.

Face-Recognition Notebook & Demo using principal component analysis.

Study Notes on machine learning, data analysis, algorithms and best practices using Python and Jupyter Notebook.

Final year project experimenting with clustering and topological data analysis of scRNA-seq data using Python and R across two Jupyter notebooks

List of Kaggle notebooks

Principal Component Analysis Example Notebook.

Notebook to Perform Market Segmentation using K-means clustering, PCA, and Auto-encoders.

Notebooks on PCA (Principal Component Analysis).

A simple Jupyter notebook to visualize data in latent space using dimensionality reduction techniques.

Mathematics for Machine Learning Notebooks and files

Pan sharpening algorithms run on Jupyter Notebook: Brovey, weighted Brovey, PCA, and simple mean.

Repository for the Wine K-Means Clustering Kaggle notebook.

Introduction to Principal Component Analysis with scikit-learn

Various Template Notebooks for Deploying ML models with Amazon Sagemaker

Jupyter notebooks implementing Machine Learning algorithms in Scikit-learn and Python

A hub that contains notebooks that implement Regression models, illustrates LR via Gradient Descent, compares K-means vs Spectral vs Hierarchical, compares PCA vs t-SNE

This Python notebook demonstrates the application of Support Vector Machines (SVM) for classification tasks on the MNIST dataset. The notebook covers data preprocessing, hyperparameter tuning, and dimensionality reduction using PCA.

This repository is a series of notebooks that show analysis and modeling of the Breast Cancer data from Kaggle.

This repository contains a highly detailed notebook that serves as an assignment for the Data Analysis course at the Higher School of Computer Science ESI. The notebook covers the topic of PCA (Principal Component Analysis), providing thorough explanations and examples.