Course Highlights
Developers must become proficient in LangChain and LLM (Large Language Models) in the rapidly evolving tech industry of today. This course is a great method to advance your career because of the abundance of chances in the field of LLM applications brought about by the growing demand for AI-driven solutions. This course will walk you through the nuances of LangChain and show you how to use LLM to create creative applications, regardless of your level of experience as a developer. You can stay ahead in a subject that is changing quickly if you comprehend the fundamental ideas of LangChain.
Professionals with experience with LLM-based apps and LangChain are in more demand on the UK employment market. Because AI is being used so widely in sectors like e-commerce, healthcare, and finance, businesses are looking for specialists who can incorporate state-of-the-art language models into their systems. The need for LangChain specialists is anticipated to increase as more companies use these technologies, providing better pay and intriguing employment opportunities. You will be prepared to close this gap and seize the many job chances after completing this course.
After finishing the course, you will have the ability to create complex, scalable applications using LLM technologies. You’ll learn how to build and implement chains, manage memory, optimise data retrieval, and integrate APIs, among other essential skills. The hands-on experience you earn will position you as a top candidate in the competitive job market, preparing you for future challenges and professional advancement.
Learning outcome
- Understand the fundamentals of LangChain and its application in LLM-powered systems.
- Develop basic and advanced chains for building LLM applications.
- Implement memory management to enhance LLM-powered applications.
- Utilize OpenAI Function Calling for seamless integration with language models.
- Leverage Retrieval Augmented Generation (RAG) for dynamic content generation.
- Integrate LangChain Expression Language (LCEL) to streamline LLM operations.
Course media
Why should I take this course?
- Learn how to develop LLM-powered applications using LangChain, a highly sought-after skill in AI.
- Gain proficiency in advanced concepts such as RAG, memory management, and LangChain Expression Language.
- Enhance your career prospects by mastering tools and techniques used in cutting-edge AI applications.
- Increase your earning potential with skills that are in high demand across tech industries in the UK.
Certificate of Achievement
Skill Up Recognised Certificate
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CPD Quality Standards Accredited Certificate
The CPD Quality Standards Accredited Certificate of Achievement is available for application when you successfully finish the Digital Marketing Certificate Course.
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Requirements
- A basic understanding of programming languages like Python.
- Familiarity with machine learning and AI concepts.
- No prior knowledge of LangChain required, but an interest in AI is beneficial.
Career Path
- Junior LLM Developer: £40,000 - £55,000
- LangChain Developer: £55,000 - £75,000
- Senior LLM Engineer: £75,000 - £90,000
- AI Application Architect: £90,000 - £110,000
Course Curriculum
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Why this course is different00:01:00
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Prerequisites00:01:00
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Essential topics and terms (theory)00:04:00
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Why this course does not cover Open Source models like LLama200:01:00
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Optional: Install Visual Studio Code00:02:00
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Get the source files with Git from Github00:02:00
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Create OpenAI Account and create API Key00:02:00
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Setup of a virtual environment00:03:00
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Setup OpenAI Api-Key as environment variable00:03:00
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Exploring the vanilla OpenAI package00:03:00
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LLM Basics00:07:00
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Prompting Basics00:02:00
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Theory: Prompt Engineering Basics00:02:00
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Few Shot Prompting00:05:00
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Chain of thought prompting00:02:00
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Pipeline-Prompts00:04:00
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Prompt Serialisation00:03:00
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Introduction to chains00:01:00
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Basic chains – the LLMChain00:03:00
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Response Schemas and OutputParsers00:06:00
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LLMChain with multiple inputs00:02:00
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SequentialChains00:04:00
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RouterChains00:04:00
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Callbacks00:05:00
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Memory basics – ConversationBufferMemory00:04:00
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ConversationSummaryMemory00:03:00
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EXERCISE: Use Memory to build a streamlit Chatbot00:01:00
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SOLUTION: Chatbot with Streamlit00:03:00
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OpenAI Function Calling – Vanilla OpenAI Package00:08:00
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Function Calling with LangChain00:04:00
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Limits and issues of the langchain Implementation00:03:00
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RAG – Theory and building blocks00:03:00
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Loaders and Splitters00:04:00
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Embeddings – Theory and practice00:04:00
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VectorStores and Retrievers00:07:00
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RAG Service with FastAPI00:05:00
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Agents Basics – LLMs learn to use tools00:06:00
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Agents with a custom RAG-Tool00:07:00
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ChatAgents00:03:00
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Indexing API – keep your documents in sync00:02:00
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PREREQUISITE: Docker Installation00:01:00
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Setup of PgVector and RecordManager00:04:00
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Indexing Documents in practice00:06:00
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Document Retrieval with PgVector00:03:00
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Introduction to LangSmith (User Interface and Hub)00:02:00
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LangSmith Projects00:07:00
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LangSmith Datasets and Evaluation00:13:00
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Introduction to Microservice Architecture00:04:00
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How our Chatbot works in a Microservice Architecture00:02:00
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Introduction to Docker00:05:00
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Introduction to Kubernetes00:02:00
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Deployment of the LLM Microservices to Kubernetes00:13:00
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Intro to LangChain Expression Language00:01:00
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LCEL Part 1 – Pipes and OpenAI Function Calling00:02:00
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LCEL – Part 2 – VectorStores, ItemGetter, Tools00:06:00
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LCEL – Part 3 – Arbitrary Functions, Runnable Interface, Fallbacks00:07:00
14-Day Money-Back Guarantee
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Duration:3 hours, 37 minutes
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Access:1 Year
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Units:56
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