Requirements
- Basic Python knowledge, including variables, functions, loops and data structures.
- An interest in AI applications and problem-solving.
- Understanding of Machine Learning and Deep Learning Algorithms
Features
- Practical Python-Based Learning
- Understand Large Language Models (LLMs)
- Build AI Chatbots and Assistants
- Integrate AI Models Through APIs
- Work with Documents and Information
- Explore Embeddings, Vector DB and RAG
- Develop Practical AI Projects
- Responsible AI Development
Featured Review
Great experience. Learned Gen AI concepts and built my chatbots and LLM apps.
Build AI-Powered Applications Using Python, Large Language Models and Modern AI Tools
Learn how to use Python to develop applications powered by Generative AI. Explore how large language models work, connect Python applications to AI models through APIs, build intelligent chatbots and create solutions that generate, retrieve and process information.
The OCTM Generative AI with Python course introduces the concepts and techniques needed to develop AI-powered applications. You will explore large language models, prompt design, API integration and conversational applications, then progress towards techniques such as embeddings, vector databases and retrieval-augmented generation (RAG)
What You’ll Learn From This Course
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Understand Generative AI and LLMs: Explain the role of large language models and how they generate responses from input instructions and context.
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Work with Python for AI development: Use Python to structure application logic and interact with AI services.
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Design effective prompts: Develop instructions and provide context to guide AI model outputs.
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Integrate Frameworks and Libraries: Connect Python with Langchain, ChromaDB, Pinecone, Flask and StreamLit.
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Develop conversational applications: Understand the components needed to create AI chatbots and assistants.
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Work with information and documents: Explore approaches for processing content and providing relevant information to AI applications.
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Understand embeddings and vector databases: Learn how semantic representations and vector search can help applications retrieve relevant information.
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Explore retrieval-augmented generation: Understand how retrieved external information can be provided to an LLM to help produce more relevant, grounded answers.
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Evaluate and improve AI outputs: Consider accuracy, reliability, privacy and responsible AI practices when developing applications.
Upon completing this Gen AI Course, you will be ready to build Chatbots, Q&A Assistants and LLM powered apps
What Can You Build with Gen AI?
- AI Chatbot: Develop a conversational interface that accepts user questions and generates responses using a connected language model.
- Document Question-Answering Assistant: Explore how an AI application can retrieve relevant passages from selected documents and use them to help answer user questions.
- AI Writing and Summarisation Tool: Use a language model to draft content, summarise text or transform information into a requested format.
- Translation, Text-to-Audio, Text-to-Image Apps: Use a language model to translate text, convert text to audio narration and generate images from prompts.
Who Should Join?
- Python Developers: Expand your programming skills by exploring LLM integration, AI APIs and intelligent application development.
- Aspiring AI Engineers: Build an understanding of the technologies and techniques used to create applications powered by Generative AI.
- Chatbot Developers: Build an understanding of the technologies and techniques used to create applications powered by Generative AI.
Curriculum
- 12 Sections
- 70 Lessons
- 4 Days
Expand all sectionsCollapse all sections
- Module 1: Introduction to Generative AI6 ItemsUnderstand Generative AI, common applications, large language models and the differences between traditional programming and generative systems.
- 1.1What Is Artificial Intelligence and Generative AI?
- 1.2Generative AI vs Traditional AI and Machine Learning
- 1.3Understanding Large Language Models (LLMs)
- 1.4How Generative AI Models Generate Text and Content
- 1.5Understanding Tokens, Context Windows and Model Limitations
- 1.6Real-World Applications of Generative AI
- Module 2: Python Frameworks for Generative AI7 ItemsReview the Python concepts and frameworks required for AI development.
- Module 3: Vector DB and Embeddings Fundamentals4 ItemsUnderstand Gen-AI pipeline, data pre-processing, data representation and vectorization.
- Module 4: Prompt Engineering5 ItemsExplore prompt structure, instructions, context, output formats and techniques for improving the usefulness of model responses.
- Module5: Mastering Vector Databases5 ItemsUnderstand how text embeddings represent information and how vector search can help retrieve content that is relevant to a user's question.
- Module 6: Integrating Generative AI APIs with Python8 ItemsLearn how applications send requests to AI services, handle responses, manage errors and incorporate model outputs into application workflows.
- 6.1Introduction to Generative AI APIs
- 6.2API Keys, Authentication and Secure Configuration
- 6.3Sending Requests to a Language Model Using Python
- 6.4Understanding API Parameters and Model Settings
- 6.5Processing and Displaying Model Responses
- 6.6Handling Errors, Timeouts and Rate Limits
- 6.7Managing Token Usage and API Costs
- 6.81. Building a Python Application Using a Generative AI API
- Module 7: Building AI Chatbots and Assistants8 ItemsExplore conversational application design, managing conversation context and developing assistants for defined tasks.
- 7.1Understanding Chatbots, AI Assistants and Conversational AI
- 7.2Designing a Chatbot Workflow and Conversation Structure
- 7.3Connecting a Chatbot to an LLM Using Python
- 7.4Managing Conversation History and Context
- 7.5Designing System Instructions and Assistant Behavior
- 7.6Handling Follow-Up Questions and Contextual Conversations
- 7.7Connecting Chatbots to Documents and Knowledge Sources
- 7.8Practical Project: Build a Python-Powered AI Assistant
- Module 8: Retrieval-Augmented Generation (RAG)10 ItemsExplore how to retrieve information from a selected knowledge source and supply it to an LLM to support context-aware responses and document question-answering applications.
- 8.1Introduction to Retrieval-Augmented Generation
- 8.2Understanding the RAG Architecture and Workflow
- 8.3Preparing and Cleaning Documents for Retrieval
- 8.4Splitting Documents into Chunks
- 8.5Generating Embeddings for Document Content
- 8.6Storing Document Embeddings in a Vector Database
- 8.7Retrieving Relevant Information for User Queries
- 8.8Generating Answers Grounded in Retrieved Sources
- 8.9Handling Missing Information and Hallucination
- 8.10Practical Project: Build a Document Question-Answering Assistant
- Module 9: Testing, Evaluation and Responsible AI6 ItemsExplore methods for checking AI outputs, handling errors, protecting sensitive information and improving the reliability of AI-powered applications.
- 9.1Common Errors and Limitations of Generative AI
- 9.2Understanding Hallucinations and Unsupported Responses
- 9.3Evaluating Answer Relevance, Accuracy and Consistency
- 9.4Testing Prompts and Comparing Model Outputs
- 9.5Monitoring Performance, Latency and Token Costs
- 9.6Responsible AI: Fairness, Transparency and Human Oversight
- Module 10: Mastering OpenAI with Python2 ItemsCreate chatbots, audio transcripts and images using OpenAI GPT models.
- Module 11: Mastering Open Source LLM with Hugging Face8 ItemsUse open-source LLM on Google Colab for building Gen-AI applications.
- 11.1Introduction to Hugging Face and the Open-Source AI Ecosystem
- 11.2Exploring the Hugging Face Model Hub
- 11.3Understanding Model Cards, Licences and Hardware Requirements
- 11.4Setting Up Python and the Transformers Library
- 11.5Loading Pretrained Models and Tokenizers
- 11.6Generating Text with Open-Source Language Models
- 11.7Running Models Locally vs Using Hosted Inference
- 11.8Practical Project: Build an AI Application Using an Open-Source LLM
- Module 12: Final Project1 ItemComplete medical chatbot using LLM, Pinecoce VectorDB, RAG, Flask
Generative AI with Python involves using Python to build applications that connect to AI models capable of generating text or other outputs. Typical examples include chatbots, writing assistants and document question-answering tools.
Delegates must have a basic understanding of Python.
Yes, You will learn how to build AI chatbots, Q&A assistants and document analyzer.
A large language model (LLM) like ChatGPT is an AI model trained to process and generate language. LLMs can support tasks such as answering questions, summarising text, drafting content and generating code.
Embeddings represent information as numerical vectors that capture aspects of its meaning. Vector databases store and search these representations, helping applications retrieve information relevant to a query.
RAG combines information retrieval with language generation. An application retrieves relevant information from an external source and provides it to an LLM as context when generating a response.
Generative AI focuses on producing content or responses. Agentic AI systems can use AI models, tools and workflow logic to pursue goals through multiple steps. They are related fields, and Generative AI models can form part of an AI agent.
Training includes hands-on live python coding sessions, concepts understanding with examples, study material (PDF) and training certificate from our UK institute.
This Gen-AI course takes 4-days to complete.
You can directly WhatsApp +44 7441 426446 or fill the registration form.
Oxford College of Technology and Management is a leading training provider for AI, known for its updated course offerings and expert instructors. Our courses are interactive, allowing the delegates to have the same quality learning experience as a classroom session.
Yes, delegates will be provided a certificate of completion from Oxford College of Technology and Management, London - UK.
Oxford College of Technology and Management is registered and approved by UK government at Companies House in England and Wales. Registration # 16434551 - Approved education provider by The UK Register of Learning Providers (UKRLP) # 10098382
Oxford College is an approved education provider by European Union Education Commission. Approval # E10401966 - Approved education vendor of European Commission. Approval # 871352941
Lessons:
