A linear algebra and machine learning in Scala hands-on based on a Databricks community cloud notebook using Breeze and Spark MLlib.

Face-Recognition Notebook & Demo using principal component analysis.

Notebooks on PCA (Principal Component Analysis).

Principal Component Analysis Example Notebook.

Various Template Notebooks for Deploying ML models with Amazon Sagemaker

Mathematics for Machine Learning Notebooks and files

List of Kaggle notebooks

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

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

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

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

Repository for the Wine K-Means Clustering Kaggle notebook.

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

A.I. and Machine Learning notebooks: Using Supervised Learning, Unsupervised Learning, Re-enforcement Learning to solve Classification, Clustering and Regression problems

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

This repository contains introductory notebooks for principal component analysis.

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

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.

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.