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Classroom training and venue arrangements depend on the availability of a suitable conference hall in Chicago, United States. Dates, seating capacity, facilities and the final venue are confirmed before booking.
Chicago, United States
Generative AI
Embeddings & Semantic Searchin Chicago
Master Embeddings & Semantic Search with practical training designed to build AI-powered search and retrieval solutions. Learn text embeddings, vector representations, similarity measures, semantic retrieval, embedding models, search…
40 Hours/5 daysLive virtual trainingFoundation
Classroom training and venue arrangements depend on the availability of a suitable conference hall in Chicago, United States. Dates, seating capacity, facilities and the final venue are confirmed before booking.
Learning in Chicago, United States
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Explore Embeddings & Semantic Search
Master Embeddings & Semantic Search with practical training designed to build AI-powered search and retrieval solutions. Learn text embeddings, vector representations, similarity measures, semantic retrieval, embedding models, search optimization, and real-world AI applications. Develop hands-on skills for integrating embeddings with RAG, LLMs, and vector databases. This industry-focused course helps learners build scalable semantic search solutions and advance their AI and Generative AI skills in Chicago, United States .
View the complete course informationCourse curriculum
- Module 1: Foundations of Embeddings and Semantic Search
- Introduction to embeddings and semantic representations
- Evolution from keyword-based search to semantic search
- Applications of embeddings in modern AI systems
- Overview of text, image, and multimodal embeddings
- Module 2: Understanding Vector Representations
- Vectors, dimensions, and numerical representations
- Dense and sparse vector representations
- Understanding vector spaces and semantic relationships
- Distance and similarity concepts
- Module 3: Text Processing for Embeddings
- Text cleaning and normalization
- Tokenization and preprocessing techniques
- Document segmentation and chunking strategies
- Handling metadata and contextual information
- Module 4: Embedding Models
- Overview of popular embedding models
- Open-source and API-based embedding models
- Sentence and document embeddings
- Selecting embedding models based on application requirements
- Model limitations and trade-offs
- Module 5: Similarity Search and Distance Metrics
- Cosine similarity
- Euclidean distance
- Dot-product similarity
- Nearest-neighbor search
- Top-K retrieval and relevance ranking
- Module 6: Building Semantic Search Solutions
- Designing an end-to-end semantic search workflow
- Generating embeddings from documents
- Storing and managing vector representations
- Performing semantic queries
- Ranking and presenting search results
- Module 7: Vector Databases and Retrieval
- Introduction to vector databases
- Working with vector indexes and collections
- Metadata filtering and hybrid retrieval
- Integration with Pinecone, Weaviate, and Milvus
- Managing vector data for scalable applications
- Module 8: Semantic Search with RAG and LLMs
- Introduction to Retrieval-Augmented Generation
- Using embeddings for document retrieval
- Connecting semantic search with LLM applications
- Context selection and retrieval pipelines
- Building knowledge-based AI applications
- Module 9: Search Quality and Optimization
- Measuring retrieval relevance and accuracy
- Improving chunking and embedding strategies
- Handling irrelevant and duplicate results
- Optimizing search latency and performance
- Evaluating retrieval quality
- Module 10: Real-World Implementation and Capstone Project
- Designing an industry-oriented semantic search application
- Implementing document ingestion and embedding generation
- Building semantic retrieval and ranking workflows
- Integrating the solution with an AI application
- Testing, evaluation, and optimization
- Healthcare-based semantic search implementation in ChicagoUnited States
Exam details
- Exam Level: Beginner to Intermediate – AI & Generative AI
- Exam Type: Online computer-based assessment consisting of multiple-choice questions and practical, scenario-based questions.
- Number of Questions: 40 questions
- Exam Duration: 90 minutes
- Passing Score: 70% (28 out of 40 questions)
- Assessment Areas: Embeddings, vector representations, embedding models, similarity metrics, semantic search, retrieval techniques, vector databases, RAG concepts, search evaluation, and optimization.
- Practical Assessment: Candidates may be evaluated on embedding generation, similarity search, semantic retrieval, metadata filtering, and designing an effective search workflow.
- Scenario-Based Questions: Healthcare and enterprise-oriented scenarios may be included to assess the ability to select appropriate embedding and semantic search approaches.
- Certification: Candidates who successfully complete the assessment and meet the course requirements receive a StepMerit Course Completion Certificate.
- Assessment Platform: The examination is conducted through an online assessment platform accessible to learners in ChicagoUnited States.
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Who this course is for
AI and Machine Learning Professionals Generative AI Developers Data Scientists and Data Analysts NLP Engineers and Practitioners Software and Application Developers RAG and LLM Application Developers Machine Learning Engineers Search and Information Retrieval Professionals AI Solutions Architects Technology and IT Professionals AI Career Switchers and Enthusiasts Professionals Building AI Solutions
Before you begin
Python Programming Knowledge – Basic understanding of Python, functions, classes, packages, data structures, and API calls is recommended for practical embedding and semantic search development. Artificial Intelligence Fundamentals – Familiarity with Artificial Intelligence, Machine Learning, and basic Generative AI concepts will help learners understand how embeddings support AI-powered applications. Natural Language Processing Basics – Basic knowledge of text processing, tokens, text similarity, and Natural Language Processing (NLP) concepts is beneficial for understanding semantic representations. Embeddings Concepts – A basic understanding of vectors, numerical representations, text…
Learning outcomes
Problem Statement – Banks manage large volumes of product documents, loan policies, account information, compliance guidelines, FAQs, and customer-support content. Traditional keyword-based search may return irrelevant results when customers or employees use different words to express the same banking requirement. Identifying the Search Challenge – Learners analyzed scenarios such as a customer searching for “ways to reduce my home loan interest” while relevant documents may use terms such as “mortgage rate reduction,” “loan refinancing,” or “interest-rate options.” This demonstrated the limitations of exact keyword matching. How We Overcame the Problem – Learners implemented an…
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Insights for Embeddings & Semantic Search
Embeddings & Semantic Search: A Practical Guide to AI-Powered Search
As AI applications continue to evolve, organizations need better ways to search and retrieve information from large volumes of unstructured data. Traditional keyword-based search often struggles when users express the same idea using different words. Embeddings and semantic search address this challenge by enabling…
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