Introduction to Machine Learning and Advanced LLM Techniques (AILLM)

AI - Artificial Intelligence, AI for developers

The course "Introduction to Machine Learning and Advanced LLM Techniques" will teach you how to work with modern AI tools, such as large language models (LLMs), the Ollama system, and LangChain. You will learn how machine learning works and how it differs from deep learning, how to deploy and optimize LLM models both locally and in the cloud, and explore AI capabilities for text generation, data analysis, and programming. You will gain practical skills that will enable you to effectively use artificial intelligence in your own projects.

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The course:

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• Introduction to Machine Learning
1. Definition and importance of machine learning
2. Key concepts and terminology

• Machine Learning vs. Deep Learning
1. Differences between traditional machine learning and deep learning
2. Use cases for each approach

• Large Language Models (LLM)
1. Introduction to LLMs, definition
2. LLM – Theoretical overview (applications and benefits of LLMs, transformer-based neural network, how to train on big data, model parameters – the bigger the smarter?)

• What can LLM do?
1. Text generation - writing articles, essays, poems, scripts, code
2. Translations and language analysis - improving the quality of automatic translations
3. Chatbots and virtual assistants - e.g. ChatGPT, Claude, Gemini
4. Data summarization and analysis - LLMs can analyze documents and extract relevant information
5. Programming – they can generate and correct code in different languages

• Introduction to Ollama
1. Step-by-step guide to installing Ollama
2. Troubleshooting common installation issues

• Hardware and Software Requirements
1. System specifications for running Ollama
2. Compatibility with different operating systems

• Running LLM Models Locally
1. Setting up a local environment for LLM models
2. Best practices for local deployment

• GPU Acceleration
1. Benefits of using GPUs for machine learning tasks
2. Configuring GPU acceleration in Ollama

• Using Ollama with Python
1. Introduction to integrating Ollama with Python
2. Key advantages and use cases

• REST API
1. Understanding the REST API provided by Ollama
2. Making API calls from Python

• Python Libraries
1. Essential Python libraries for working with Ollama
2. Examples of common tasks using these libraries

• LangChain Introduction
1. What is LangChain?
2. Key features and benefits

• Using LangChain from Python
1. Setting up LangChain in a Python environment
2. Basic operations and examples

• Using LangChain with Ollama
1. Integrating LangChain with Ollama for enhanced functionality
2. Practical use cases and demonstrations

• Prompt Templates
1. Creating and using prompt templates in LangChain
2. Best practices for effective prompts

• Output Formatting and Processing
1. Techniques for formatting and processing outputs from LangChain
2. Handling different types of output data

• RAG Introduction (Retrieval-Augmented Generation)
1. What is RAG?
2. How it enhances LLM capabilities

• Loading Documents Using LangChain and Ollama
1. Techniques for loading and managing documents in LangChain and Ollama
2. Best practices for document handling

• Embeddings
1. Understanding embeddings in the context of RAG
2. Creating and using embeddings effectively

• ChatBot Sample Application
1. Introduction to building a chatbot application
2. Key components and architecture

• Implementation Steps
1. Step-by-step guide to creating a chatbot using Ollama, LangChain, and RAG
2. Integrating various modules for a cohesive solution

• Testing and Deployment
1. Testing the chatbot for functionality and performance
2. Deploying the chatbot in different environments

Assumed knowledge:

Basics of Programming

Schedule:

1 day (9:00 AM - 5:00 PM )

Course price:

316.00 € ( 382.36 € incl. 21% VAT)

Language: