Weekend batches
October 2026
09:00–15:00 · flexible hours
$799per learner
24 Oct 2026
4 of 25 seats available
31 Oct 2026
10 of 25 seats available
Generative AI
Embeddings & Semantic Search in Serbia
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 Serbia .
40 Hours/5 daysFoundation4.3 · 1,000 reviews
Training calendar
Upcoming four-month course schedule
A new weekday and weekend batch starts every week. Each batch runs for 5 training days, based on the course duration of 40 Hours/5 days.
Weekend batches
November 2026
09:00–15:00 · flexible hours
$799per learner
07 Nov 2026
25 of 25 seats available
14 Nov 2026
25 of 25 seats available
21 Nov 2026
25 of 25 seats available
28 Nov 2026
25 of 25 seats available
Weekend batches
December 2026
09:00–15:00 · flexible hours
$799per learner
05 Dec 2026
25 of 25 seats available
12 Dec 2026
25 of 25 seats available
19 Dec 2026
25 of 25 seats available
26 Dec 2026
25 of 25 seats available
Weekend batches
January 2027
09:00–15:00 · flexible hours
$799per learner
02 Jan 2027
25 of 25 seats available
09 Jan 2027
25 of 25 seats available
16 Jan 2027
25 of 25 seats available
23 Jan 2027
25 of 25 seats available
30 Jan 2027
25 of 25 seats available
Course curriculum
What you’ll learn
40 Hours/5 daysFoundation
- 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 SerbiaSerbia
Entry requirements and prerequisites
- 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 embeddings, and similarity measures is helpful for learning how textual information is converted into meaningful vector representations.
- Large Language Model Fundamentals – Familiarity with Large Language Models (LLMs), prompts, and AI-powered applications is recommended for understanding how embeddings are used with modern AI solutions.
- Semantic Search Fundamentals – Basic awareness of keyword search, semantic search, information retrieval, and relevance ranking will help learners understand the advantages of meaning-based search.
- REST API Knowledge – Familiarity with REST APIs, JSON, authentication, API keys, and making API requests is useful when working with embedding models and AI services.
- Development Environment Familiarity – Experience with Python environments, package installation, Jupyter Notebook or similar development tools, command-line utilities, and Git is recommended for hands-on exercises in SerbiaSerbia.
- Basic Database Knowledge – Understanding of databases, records, indexing, querying, and data organization is helpful for learners who want to integrate embeddings and semantic search with vector databases.
- RAG Awareness – Basic knowledge of Retrieval-Augmented Generation (RAG) is beneficial for understanding how embeddings and semantic search can be used to retrieve relevant information for AI applications.
Career value
Reasons to choose this course
Industry-Relevant Skills
Build practical expertise in embeddings, semantic search, and AI-powered information retrieval for modern applications.
Hands-On Learning
Practice embedding generation, similarity search, indexing, retrieval, and search optimization through practical exercises.
RAG Application Focus
Learn how embeddings and semantic search form the retrieval foundation of RAG and LLM-powered applications.
Real-World Use Cases
Explore practical applications across healthcare, finance, e-commerce, enterprise search, customer support, and knowledge management.
Practical AI Integration
Learn to integrate embedding models, APIs, vector databases, and AI applications into complete search workflows.
Career-Oriented Training
Develop skills relevant to AI Engineer, ML Engineer, GenAI Developer, RAG Developer, and AI Solutions roles.
Advantages
Turn learning into practical capability
Understand Semantic Representation
Learn how words, sentences, documents, and other data are transformed into meaningful numerical representations that enable machines to understand relationships between information.
Optimize Search Relevance
Develop the ability to improve search results by applying similarity measures, distance metrics, ranking techniques, and appropriate retrieval strategies.
Work with Multiple Embedding Models
Explore different embedding approaches and understand how to select suitable models based on data type, application requirements, accuracy, and performance.
Handle Large-Scale Information Retrieval
Learn techniques for organizing and retrieving information efficiently from large collections of unstructured text and knowledge sources.
Evaluate Embedding and Retrieval Quality
Gain practical knowledge of evaluating semantic similarity, retrieval accuracy, relevance, latency, and other factors that influence search performance.
Prepare for Advanced AI Systems
Build a strong technical foundation for developing intelligent applications such as recommendation systems, document discovery platforms, enterprise search, chatbots, and RAG-based solutions.
Exam format & assessment
- 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 SerbiaSerbia.
Bring your learning together
Course conclusion
The Embeddings & Semantic Search course provides practical, industry-focused knowledge for building intelligent search and AI-powered retrieval solutions. Learners develop hands-on expertise in text embeddings, vector representations, similarity search, embedding models, retrieval optimization, vector databases, and RAG integration. Through practical exercises and a healthcare-based capstone project, participants gain the skills needed to develop meaningful semantic search applications. This training helps professionals strengthen their AI and Generative AI capabilities and prepare for emerging opportunities in SerbiaSerbia.
Target audience
- 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
Verified learning evidence
Expertise, recognition and learner outcomes
Course-specific 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 Coverage: Hands-on training covering text embeddings, embedding models, cosine similarity, vector representations, semantic retrieval, Top-K search, metadata filtering, and retrieval evaluation.
- AI Application Experience: Guided learners in integrating embeddings and semantic search with LLMs, RAG pipelines, vector databases, and AI-powered applications.
- Industry Use Cases: Delivered practical implementations involving banking, healthcare, finance, enterprise search, customer support, knowledge management, and recommendation systems.
- Hands-On Project Guidance: Supported learners in developing end-to-end semantic search solutions covering document processing, chunking, embedding generation, vector storage, retrieval, ranking, and evaluation.
- Performance & Evaluation Experience: Assessed learners through practical exercises, technical assignments, scenario-based activities, and implementation projects to measure their understanding of semantic search concepts.
- Trainer Experience Summary: 5+ Years Experience | 100+ Training Hours | 150+ Candidates Trained | Embeddings & Semantic Search | RAG & LLM Integration | Hands-On Projects | Industry Use Cases
Documented learner 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 embedding-based semantic search approach that converts banking documents and user queries into numerical vector representations. The system compares these vectors to identify information with similar meaning and context.
- Document Processing – Learners collected sample banking documents such as loan policies, credit-card information, account-service guidelines, FAQs, and financial-product documentation, followed by text cleaning and preprocessing.
- Document Chunking – Large banking documents were divided into meaningful sections or chunks so that the retrieval system could identify specific information instead of returning entire documents.
- Embedding Generation – Learners generated embeddings for the processed document chunks using an appropriate embedding model and created vector representations that captured the semantic meaning of banking information.
- Semantic Search Implementation – User queries were converted into embeddings and compared with stored document vectors using similarity techniques such as cosine similarity, allowing the system to retrieve relevant banking information even when the wording differed.
- Similarity and Ranking – Learners implemented Top-K retrieval and similarity scoring to rank the most relevant banking documents or content according to the user's query.
- Metadata Filtering – Learners applied metadata such as product type, document category, customer segment, and information source to improve the accuracy and relevance of search results.
- Overall Implementation – The complete solution followed the workflow: Banking Documents → Preprocessing → Chunking → Embedding Generation → Vector Storage → Query Embedding → Similarity Search → Ranking → Relevant Banking Information.
- RAG Integration – Learners demonstrated how the semantic search component can provide relevant banking context to an LLM-based application, creating the retrieval layer for a banking knowledge assistant or customer-support solution.
- Problem-Solving Evidence – Learners addressed challenges such as irrelevant search results, different terminology, lengthy documents, duplicate information, and retrieval accuracy through improved chunking, embedding selection, similarity methods, and metadata filtering.
- Practical Evaluation – Learners tested sample banking queries and evaluated whether the retrieved documents correctly addressed customer requirements, helping them understand retrieval relevance and search-quality optimization.
- Final Evidence of Learning – Learners produced a working banking semantic-search prototype, processed document dataset, embedding workflow, vector search implementation, sample queries, retrieved results, evaluation observations, and implementation documentation.
- Overall Learner Outcome – By completing the banking scenario, learners demonstrated practical competency in embeddings, semantic search, similarity retrieval, document processing, vector-based information retrieval, RAG integration, and search optimization, preparing them to develop intelligent search solutions for modern banking and financial-services applications.
Helpful answers for planning your training
Frequently asked questions
It teaches how to use embeddings and semantic retrieval to build intelligent AI-powered search solutions.
Text embeddings are numerical vector representations that capture the semantic meaning of text.
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