AI-Powered Search, Video Edition
AI-Powered Search, Video Edition is a course on how to build intelligent search systems with the ability to understand and retrieve information, published by Oreilly Online Academy. This comprehensive course explores how artificial intelligence can transform traditional search systems into smarter, context-aware, and highly efficient tools. Learners will understand the fundamentals of search engines, natural language processing, and ranking algorithms, while discovering how AI models enhance query understanding, personalization, and relevance of results. The course combines theory with practical implementation to demonstrate how AI is reshaping information retrieval across industries.This course covers search engine fundamentals, natural language processing for queries, semantic search techniques, ranking algorithms, vector embedding, personalization strategies, context-aware retrieval, integrating AI with search infrastructure, scalability considerations, real-world applications of AI-powered search, and best practices for building efficient, user-centric search systems.
103 Video Lessons
20.9 Hours On-Demand
Created by Senior Industry Specialist
Uploaded Sep 2026
English
Preview This Course
Free$129.99100% Free
Instant high-definition streaming with zero paywalls.
Course Features:
20.9 hours on-demand video
103 complete lectures
1 downloadable project zip file(s)
Streamable on mobile, tablet & desktop
Self-paced curriculum with progress tracking
Direct MP4 downloads & offline video access
Verified course archives hosted on cloud infrastructure.
What You'll Master in this Course
Introducing AI-powered search
Implementing user- and domain-specific relevance ranking
Mass communication
Introducing reflective intelligence
Automatically extracting knowledge graphs from content
Learning intent by traversing semantic knowledge graphs
Using context to learn domain-specific language
And…
Course Curriculum103 Lectures
1 sections • 20.9 hours total length
Prefer offline learning? Download all 103 video lectures and project files for free.
Download FreePart 1. Modern search relevance
Preview
2m
MP4Chapter 1. Understanding user intent
Preview
19m
MP4Chapter 1. Summary
3m
MP4Chapter 1. How does AI-powered search work
34m
MP4Chapter 1. Introducing AI-powered search
22m
MP4Chapter 2. The structure of natural language
6m
MP4Chapter 2. Working with natural language
26m
MP4Chapter 2. Modeling domain-specific knowledge
10m
MP4Chapter 2. Distributional semantics and embeddings
18m
MP4Chapter 2. Summary
2m
MP4Chapter 2. Content + signals The fuel powering AI-powered search
6m
MP4Chapter 2. Challenges in natural language understanding for search
16m
MP4Chapter 3. Ranking and content-based relevance
36m
MP4Chapter 3. Implementing user and domain-specific relevance ranking
4m
MP4Chapter 3. Summary
2m
MP4Chapter 3. Controlling the relevance calculation
30m
MP4Chapter 4. Summary
2m
MP4Part 2. Learning domain-specific intent
3m
MP4Chapter 4. Crowdsourced relevance
30m
MP4Chapter 4. Introducing reflected intelligence
35m
MP4Chapter 5. Knowledge graph learning
9m
MP4Chapter 5. Automatically extracting knowledge graphs from content
13m
MP4Chapter 5. Using our search engine as a knowledge graph
3m
MP4Chapter 5. Using knowledge graphs for semantic search
2m
MP4Chapter 5. Summary
2m
MP4Chapter 5. Learning intent by traversing semantic knowledge graphs
50m
MP4Chapter 6. Using context to learn domain-specific language
10m
MP4Chapter 6. Query-sense disambiguation
12m
MP4Chapter 6. Learning related phrases from query signals
25m
MP4Chapter 6. Phrase detection from user signals
7m
MP4Chapter 6. Misspellings and alternative representations
16m
MP4Chapter 6. Pulling it all together
2m
MP4Chapter 6. Summary
1m
MP4Chapter 7. Interpreting query intent through semantic search
11m
MP4Chapter 7. Indexing and searching on a local reviews dataset
6m
MP4Chapter 7. An end-to-end semantic search example
4m
MP4Chapter 7. Summary
2m
MP4Part 3. Reflected intelligence
3m
MP4Chapter 8. Signals-boosting models
4m
MP4Chapter 7. Query interpretation pipelines
37m
MP4Chapter 8. Normalizing signals
5m
MP4Chapter 8. Fighting signal spam
10m
MP4Chapter 8. Combining multiple signal types
7m
MP4Chapter 8. Time decays and short-lived signals
13m
MP4Chapter 8. Index-time vs. query-time boosting Balancing scale vs. flexibility
23m
MP4Chapter 8. Summary
2m
MP4Chapter 9. Recommendation algorithm approaches
12m
MP4Chapter 9. Personalized search
11m
MP4Chapter 9. Summary
2m
MP4Chapter 9. Implementing collaborative filtering
27m
MP4Chapter 9. Challenges with personalizing search results
8m
MP4Chapter 9. Personalizing search using content-based embeddings
33m
MP4Chapter 10. Step 1 A judgment list, starting with the training data
3m
MP4Chapter 10. Learning to rank for generalizable search relevance
13m
MP4Chapter 10. Step 2 Feature logging and engineering
10m
MP4Chapter 10. Step 4 Training (and testing!) the model
9m
MP4Chapter 10. Step 3 Transforming LTR to a traditional machine learning problem
15m
MP4Chapter 10. Steps 5 and 6 Upload a model and search
10m
MP4Chapter 10. Rinse and repeat
2m
MP4Chapter 10. Summary
2m
MP4Chapter 11. Overcoming position bias
12m
MP4Chapter 11. Automating learning to rank with click models
29m
MP4Chapter 11. Exploring your training data in an LTR system
4m
MP4Chapter 11. Handling confidence bias Not upending your model due to a few lucky clicks
18m
MP4Chapter 11. Summary
2m
MP4Chapter 12. Overcoming ranking bias through active learning
15m
MP4Chapter 12. Exploit, explore, gather, rinse, repeat A robust automated LTR loop
5m
MP4Chapter 12. AB testing a new model
18m
MP4Chapter 12. Overcoming presentation bias Knowing when to explore vs. exploit
22m
MP4Chapter 12. Summary
2m
MP4Part 4. The search frontier
3m
MP4Chapter 13. Semantic search with dense vectors
9m
MP4Chapter 13. Search using dense vectors
16m
MP4Chapter 13. Getting text embeddings by using a Transformer encoder
11m
MP4Chapter 13. Applying Transformers to search
19m
MP4Chapter 13. Semantic search with LLM embeddings
12m
MP4Chapter 13. Cross-encoders vs. bi-encoders
11m
MP4Chapter 13. Natural language autocomplete
31m
MP4Chapter 13. Quantization and representation learning for more efficient vector search
47m
MP4Chapter 13. Summary
2m
MP4Chapter 14. Constructing a question-answering training dataset
22m
MP4Chapter 14. Building the reader with the new fine-tuned model
2m
MP4Chapter 14. Question answering with a fine-tuned large language model
25m
MP4Chapter 14. Fine-tuning the question-answering model
13m
MP4Chapter 14. Incorporating the retriever Using the question-answering model with the search engine
9m
MP4Chapter 14. Summary
2m
MP4Chapter 15. Foundation models and emerging search paradigms
17m
MP4Chapter 15. Multimodal search
21m
MP4Chapter 15. Other emerging AI-powered search paradigms
8m
MP4Chapter 15. Generative search
50m
MP4Chapter 15. Hybrid search
16m
MP4Chapter 15. Convergence of contextual technologies
5m
MP4Chapter 15. All the above, please!
3m
MP4Chapter 15. Summary
2m
MP4Appendix A. Running the code examples
3m
MP4Appendix A. Pulling the source code
1m
MP4Appendix A. Building and running the code
5m
MP4Appendix A. Working with Jupyter
4m
MP4Appendix A. Working with Docker
3m
MP4Appendix B. Supported search engines and vector databases
2m
MP4Appendix B. Swapping out the engine
2m
MP4Appendix B. The engine and collection abstractions
4m
MP4Appendix B. Adding support for additional engines
2m
MP4Requirements
- Basic enthusiasm to learn and follow along with lessons
- A computer or mobile device with a modern internet connection
Description
AI-Powered Search, Video Edition is a course on how to build intelligent search systems with the ability to understand and retrieve information, published by Oreilly Online Academy. This comprehensive course explores how artificial intelligence can transform traditional search systems into smarter, context-aware, and highly efficient tools. Learners will understand the fundamentals of search engines, natural language processing, and ranking algorithms, while discovering how AI models enhance query understanding, personalization, and relevance of results. The course combines theory with practical implementation to demonstrate how AI is reshaping information retrieval across industries.This course covers search engine fundamentals, natural language processing for queries, semantic search techniques, ranking algorithms, vector embedding, personalization strategies, context-aware retrieval, integrating AI with search infrastructure, scalability considerations, real-world applications of AI-powered search, and best practices for building efficient, user-centric search systems.
Instructor
S
Senior Industry Specialist
Specialist in AI, Machine Learning & Data Science
Passionate educator focused on real-world practical skills, modern frameworks, and production-ready engineering practices. Delivering step-by-step masterclasses accessible to learners globally on MJ Accedemy.
Preview This Course
Free$129.99100% Free
Instant high-definition streaming with zero paywalls.
Course Features:
20.9 hours on-demand video
103 complete lectures
1 downloadable project zip file(s)
Streamable on mobile, tablet & desktop
Self-paced curriculum with progress tracking
Direct MP4 downloads & offline video access
Verified course archives hosted on cloud infrastructure.
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