Embeddings & Semantic Searchin Edinburgh

40 Hours/5 daysLive virtual trainingFoundation

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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 Edinburgh, United Kingdom .

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Learning from Edinburgh

Edinburgh offers a strong professional community, cultural heritage and accessible learning opportunities.

Course 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 EdinburghUnited Kingdom

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 EdinburghUnited Kingdom.

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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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Room facilities / classroom arrangements

General planning information. Availability is confirmed for your selected venue and batch.

Typical classroom arrangements — subject to confirmation for the selected batch: 1. Learning space and seating: A suitable room layout with seating and desk space for course discussions and exercises. 2. Presentation equipment: A display or projector for instructor-led explanations; equipment requirements are confirmed before delivery. 3. Internet and power: Connectivity and charging access where needed for the course’s digital resources or labs. 4. Collaboration tools: A whiteboard or flip chart for group activities, demonstrations and note-taking. 5. Room comfort: Appropriate lighting and ventilation, with breaks included in the agreed training schedule. 6. Access and learner needs: Accessibility, refreshments and any specific accommodation requirements should be discussed with the adviser in advance. This is general planning information. It does not confirm a particular venue, equipment package or included refreshments.

Course expertise

Trainer Experience: 5+ years of industry and training experience in AI, ML, NLP, Generative AI, embeddings, and semantic search technologies. Course-Specific Training Experience: Delivered 100+ hours of training covering embeddings, embedding models, vector representations, similarity search, semantic retrieval, search optimization, and RAG integration. Candidates Trained: 150+ candidates including developers, AI/ML professionals, data scientists, students, and technology professionals. Training Delivery: Conducted instructor-led online and classroom sessions combining theoretical concepts, live demonstrations, coding exercises, practical assignments, and project-based learning. Technical…

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Insights for Embeddings & Semantic Search

Learning insight 01

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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