Introduction to Machine Learning and Advanced LLM Techniques (AILLM)

Course content:

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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