Approximate Nearest Neighbor Search Courses and Certifications

Coursera

Embed Everything

Master the art of converting unstructured data into vector embeddings using pre-trained models, t-SNE visualization, and nearest-neighbor analysis to build scalable, production-ready pipelines.

• Coursera
• 3 hours 17 minutes
• Self-Paced
• Paid Course

Packt

Harnessing LLMs & Text-Embeddings API with Google Vertex AI

Master Google Vertex AI's LLMs and Text-Embeddings API for building intelligent applications with semantic search, classification, and RAG systems through hands-on projects and real-world use cases.

• Coursera
• 4 hours 16 minutes
• Self-Paced
• Paid Course

Packt

Advanced LLM Design: Retrieval, Context, and Prompts

Master advanced LLM design with RAG patterns, hybrid search, prompt engineering, and enterprise evaluation strategies to build scalable, reliable AI solutions for complex business environments.

• Coursera
• 4 hours
• Self-Paced
• Paid Course

Coursera

Unify Modalities: Cross-Modal Retrieval

Master cross-modal retrieval systems that bridge text and images using FAISS, attention mechanisms, and multimodal embeddings for production-scale AI applications.

• Coursera
• 1 hour 36 minutes
• Self-Paced
• Paid Course

Coursera

Master ANN Search

Master high-speed vector search with ANN algorithms, implementing FAISS and Annoy libraries to optimize performance for large-scale datasets in AI applications.

• Coursera
• 2 hours 25 minutes
• Self-Paced
• Paid Course

Machine Learning From Basic to Advanced

926 ratings at Udemy

Learn to create Machine Learning Algorithms in Python Data Science enthusiasts. Code templates included.

• Udemy
• 2 hours 59 minutes
• Self-Paced
• Paid Course

Machine Learning & Data Science for Beginners in Python

319 ratings at Udemy

Data Science Projects with Linear Regression, Logistic Regression, Random Forest, SVM, KNN, KMeans, XGBoost, PCA etc

• Udemy
• 1 day 8 hours 5 minutes
• Self-Paced
• Paid Course

Python Engineer

Machine Learning From Scratch in Python - Full Course With 12 Algorithms

Comprehensive Python implementation of 12 popular machine learning algorithms from scratch, covering KNN, regression, SVM, decision trees, random forests, PCA, K-means, AdaBoost, and LDA.

• YouTube
• 4 hours 55 minutes
• Self-Paced
• Free Video

Coding Tech

Building a Music Recommendation Engine

Learn to build a music recommendation engine using AudioSet dataset, embedding generation, and ANNOY algorithm. Hands-on coding and practical implementation for personalized music suggestions.

• YouTube
• 43 minutes
• Self-Paced
• Free Video

James Briggs

Locality Sensitive Hashing for Search with Shingling + MinHashing - Python

Learn efficient similarity search using Locality Sensitive Hashing (LSH) with shingling and MinHashing techniques in Python. Explore implementation steps, from data preprocessing to LSH tuning, for approximate nearest neighbor search.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

John Savill's Technical Training

Understanding Generative AI: RAG, Semantic Search, Embeddings, and Vectors

Explore key Generative AI concepts like RAG, semantic search, embeddings, and vectors. Understand their importance in leveraging AI with your data for improved information retrieval and processing.

• YouTube
• 29 minutes
• Self-Paced
• Free Video

CMU Database Group

Qdrant: Open Source Vector Search Engine and Vector Database

Explore Qdrant, an open-source vector search engine and database. Learn its architecture, capabilities, and applications in machine learning and information retrieval.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

James Briggs

How LSH Random Projection Works in Search - Python

Explore efficient similarity search using Locality Sensitive Hashing (LSH) and Random Projection. Learn how to handle large-scale data and implement approximate search techniques in Python.

• YouTube
• 19 minutes
• Self-Paced
• Free Video

James Briggs

HNSW for Vector Search Explained and Implemented with Faiss - Python

Demystifying HNSW algorithm for vector similarity search, explaining its foundations, implementation with Faiss, and optimization techniques for top-performing indexes in approximate nearest neighbor searches.

• YouTube
• 35 minutes
• Self-Paced
• Free Video

James Briggs

Metadata Filtering for Vector Search - Latest Filter Tech

Explore advanced metadata filtering techniques for vector search, including pre-filtering, post-filtering, and single-stage filtering. Learn to implement efficient filters on massive datasets using Pinecone's solution.

• YouTube
• 34 minutes
• Self-Paced
• Free Video