Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
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Updated
Apr 27, 2022 - Jupyter Notebook
Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
Study Notes on machine learning, data analysis, algorithms and best practices using Python and Jupyter Notebook.
Face-Recognition Notebook & Demo using principal component analysis.
Final year project experimenting with clustering and topological data analysis of scRNA-seq data using Python and R across two Jupyter notebooks
Principal Component Analysis Example Notebook.
This repository is a series of notebooks that show analysis and modeling of the Breast Cancer data from Kaggle.
A.I. and Machine Learning notebooks: Using Supervised Learning, Unsupervised Learning, Re-enforcement Learning to solve Classification, Clustering and Regression problems
A linear algebra and machine learning in Scala hands-on based on a Databricks community cloud notebook using Breeze and Spark MLlib.
This repository contains introductory notebooks for principal component analysis.
List of Kaggle notebooks
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.
Pan sharpening algorithms run on Jupyter Notebook: Brovey, weighted Brovey, PCA, and simple mean.
Mathematics for Machine Learning Notebooks and files
Repository for the Wine K-Means Clustering Kaggle notebook.
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
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