Integrate Single-Cell RNA-Seq Datasets in R Using Seurat - Detailed Seurat Workflow Tutorial
Comprehensive tutorial on integrating single-cell RNA-Seq datasets using Seurat in R, covering data merging, batch effect correction, and visualization techniques for improved analysis.
Clustering and Markers Identification for ScRNA-Seq - Seurat Package Tutorial
Learn to analyze single-cell RNA-seq data using Seurat package, covering data import, QC, normalization, clustering, and marker identification. Gain insights into cell interactions and create visualizations.
Practical Clustering and Topological Data Analysis
Explore clustering algorithms and topological data analysis from a data science perspective, covering techniques from k-means to DBSCAN and dimensionality reduction methods like UMAP.
CLIP, T-SNE, and UMAP - Master Image Embeddings and Vector Analysis
Master image embeddings and vector analysis techniques like CLIP, T-SNE, and UMAP. Learn to cluster MNIST images, detect duplicates, and explore essential concepts in computer vision and data science through hands-on practice.
Dimensional Reduction Using PaCMAP: From High-Dimensional Data to Vector Spaces - MNIST Case Study
Explore dimensional reduction using PaCMAP algorithm, from basic implementation to advanced applications with MNIST datasets, featuring hands-on Python coding in Colab notebooks.
Dimension Reduction - An Overview
Comprehensive overview of dimension reduction techniques, from linear methods like PCA to nonlinear approaches such as LLE, Isomap, t-SNE, and UMAP, with practical application to chemical reaction data.
Img2Vec and UMAP for High-Dimensional Data Visualization
Learn to visualize high-dimensional image data using Img2Vec for embeddings, UMAP for dimensionality reduction, and Bokeh for interactive exploration. Ideal for vision classification projects.
Monitoring LLMs in Production: Beyond the Hype - Part 2
Explore techniques for monitoring and measuring drift in unstructured data models, focusing on vector/embedding drift for image and language models in production environments.
BERTopic Explained
Explore BERTopic, an advanced topic modeling technique using transformers, UMAP, and HDBSCAN. Learn its components, implementation, and customization for organizing unstructured text data effectively.
Visualize - Bringing Structure to Unstructured Data - MLOps Podcast #258
Explore techniques for structuring and visualizing unstructured data in machine learning, from classification to Retrieval-Augmented Generation, using tools like UMAP and Renumics Spotlight to uncover patterns and evaluate model performance.
Football AI Tutorial: From Basics to Advanced Stats with Python
Learn to build an AI system for advanced football analytics using computer vision and machine learning. Covers player tracking, team identification, and stats calculation like ball possession and speed.