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Generative AI with Python · LearnSpace
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courseraПрограммирование

Generative AI with Python

Курс от Packt
Средний≈ 18.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Unlock the power of generative AI by mastering Python and working hands-on with cutting-edge tools and libraries. From building large language models (LLMs) to implementing advanced agentic systems, this course takes you on an in-depth journey through AI development. You’ll explore the essentials of LLMs, model training, parameter tuning, and the integration of advanced techniques like Retrieval-Augmented Generation (RAG) and vector databases. The interactive learning experience ensures you are not just passively absorbing information but engaging with practical coding exercises and real-world applications. The course begins with the foundational setup, including Python, IDEs, and environment configurations, before diving deep into LLMs, multimodal models, and even exploring agent-based systems. You’ll move through advanced topics such as prompt crafting, chaining models, and building intelligent systems with frameworks like crewAI and AG2. The journey concludes with model fine-tuning techniques, including Low-Rank Adaptation (LoRA), that enable you to optimize performance. This course is designed for AI enthusiasts, data scientists, and developers who want to expand their skills in generative AI. It is ideal for anyone with basic knowledge of Python who wants to build AI-driven applications. The course is suitable for those at an Intermediate level with some prior programming experience in Python. By the end of the course, you will be able to design and implement generative AI models, create complex AI workflows using chains and agents, manage vector databases, and fine-tune models to suit specific tasks and domains.

Навыки, которые вы освоите

Agentic systemsModel OptimizationFine-tuningAgentic WorkflowsRetrieval-Augmented GenerationVector DatabasesModel Context ProtocolCrewAIAI WorkflowsEmbeddingsSoftware Installation

Программа курса

15 модулей · 153 учебных материалов

01Course Introduction12 материалов

Course Introduction

Self PresentationВидеоFull Course ResourcesЧтениеCourse OverviewВидеоSystem Setup (101)Видео

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Packt - Course Instructors

Преподаватель курса

Generative AI with Python
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 18.5 ч

15 модулей

Язык: Английский

Часть программы вашего университета
System Setup: PythonВидео
System Setup: IDE (101)Видео
System Setup: How to get the materialВидео
System Setup: IDE SetupВидео
System Setup: Visual C++ Build ToolsВидео
System Setup: Environment (Coding)Видео
API Keys (101)Видео
API Keys (Coding)Видео
02Large Language Models – Introduction6 материалов

Large Language Models – Introduction

LLM IntroductionВидеоClassical NLP vs. LLMВидеоNarrow AI AchievementsВидеоModel Performance and CapabilitiesВидеоComparing Classical NLP Models and Large Language ModelsDIALOGUELarge Language Models – Introduction - AssessmentЗадание
03Large Language Models – Deep Dive18 материалов

Large Language Models – Deep Dive

Model Training ProcessВидеоModel Improvement OptionsВидеоModel ProvidersВидеоModel BenchmarkingВидеоInteraction with LLMs (Coding Intro)ВидеоInteraction with LLMs Groq (Coding)ВидеоInteraction with LLMs OpenAI (Coding)ВидеоInteraction with LLMs Gemini (Coding)ВидеоMessage TypesВидеоMessage Types ExerciseВидеоMessage Types SolutionВидеоLLM ParametersВидеоLLM Parameters (Exercise)ВидеоLLM Parameters (Solution)ВидеоModel SelectionВидеоModel CapabilitiesВидеоUnderstanding LLM Message Types and Prompt StructureDIALOGUELarge Language Models – Deep Dive - AssessmentЗадание
04Large Language Models – Types and Variants11 материалов

Large Language Models – Types and Variants

Local Use of ModelsВидеоLocal Use of Models (Coding)ВидеоLarge Multimodal ModelsВидеоLarge Multimodal Models (Coding)ВидеоLarge Video ModelsВидеоTokenizationВидеоReasoning ModelsВидеоSmall Language ModelsВидеоJailBreakingВидеоRunning and Interacting with Local and Multimodal Language ModelsDIALOGUELarge Language Models – Types and Variants - AssessmentЗадание
05Large Language Models – Chains15 материалов

Large Language Models – Chains

Prompt TemplatesВидеоPrompt Templates (Coding)ВидеоPrompt Hub (Coding)ВидеоIntroductionВидеоChains (Coding)ВидеоExercise Story Character (Exercise)ВидеоExercise Story Character (Solution)ВидеоCoding: Story Character parallel (101)ВидеоCoding: Story Character parallel (Practical)ВидеоChains with Structured Output (101)ВидеоChains with Structured Output (Coding)ВидеоRouter Chain (Coding)ВидеоRouter Chain (Exercise)ВидеоDesigning and Running Chains: Prompt Templates to Parallel & Router ChainsDIALOGUELarge Language Models – Chains - AssessmentЗадание
06Vector Databases19 материалов

Vector Databases

IntroductionВидеоData Source and Loading (101)ВидеоData Source and Loading (Coding)ВидеоData Chunking (101)ВидеоData Chunking (Coding)ВидеоEmbeddings – High Level Overview (101)ВидеоEmbeddings – Deep Dive (101)ВидеоEmbeddings Model vs. LLM (101)ВидеоEmbedding Model Types (101)ВидеоEmbeddings Introduction (Coding)ВидеоEmbeddings (Coding)ВидеоData Storing (101)ВидеоData Storing Chroma (Coding)ВидеоData Querying (101)ВидеоSimilarity Search (101)ВидеоData Querying Chroma (Coding)ВидеоData Querying FAISS (Coding)ВидеоBuilding and Querying a Vector Database PipelineDIALOGUEVector Databases - AssessmentЗадание
07Retrieval-Augmented Generation – Baseline5 материалов

Retrieval-Augmented Generation – Baseline

Baseline RAG (101)ВидеоRAG Phases (101)ВидеоBaseline RAG (Coding)ВидеоImplementing a Baseline Retrieval Augmented Generation (RAG) PipelineDIALOGUERetrieval-Augmented Generation – Baseline - AssessmentЗадание
08Retrieval-Augmented Generation – Advanced16 материалов

Retrieval-Augmented Generation – Advanced

RAG Improvements (101)ВидеоImprovements in Pre-Retrieval Phase (101)ВидеоContext Enrichment (101)ВидеоCorrective RAG (101)ВидеоHybrid RAG (101)ВидеоQuery Expansion (101)ВидеоPrompt Compression (101)ВидеоSpeculative RAG (101)ВидеоAgentic RAG (101)ВидеоRetrieval Augmented Thought (101)ВидеоPrompt Caching (101)ВидеоMultimodal RAG (101)ВидеоTable RAG (101)ВидеоTable RAG (Coding)ВидеоLevels and Improvements of Retrieval-Augmented (RAG) SystemsDIALOGUERetrieval-Augmented Generation – Advanced - AssessmentЗадание
09Agentic Systems – Overview4 материалов

Agentic Systems – Overview

Agents Introduction (101)ВидеоAgentic Frameworks (101)ВидеоUnderstanding AI Agentic Systems and FrameworksDIALOGUEAgentic Systems – Overview - AssessmentЗадание
10Agentic Systems – crewAI14 материалов

Agentic Systems – crewAI

Agent Introduction (101)ВидеоFile Dependencies (101)ВидеоExample Crew SetupВидеоcrewAI Installation (Coding)ВидеоSample Project (Coding)ВидеоHigh-Level Planning (Coding)ВидеоAgent and Task Definition (Coding)ВидеоYaml Files (Coding)Видеоmain.py (Coding)Видеоrun crew (Coding)ВидеоAdding Tools (Coding)ВидеоDebugging (Coding)ВидеоDesigning a Multi-Agent Crew with QriAIDIALOGUEAgentic Systems – crewAI - AssessmentЗадание
11Agentic Systems – AG28 материалов

Agentic Systems – AG2

AG2 Introduction (101)ВидеоConversable Agent (Coding)ВидеоTwo Agent Conversation (Coding)ВидеоHuman in the Loop (Coding)ВидеоAdding Tools (Coding)ВидеоGroup Chat (Coding)ВидеоGetting Started with AG2 Agentic FrameworkDIALOGUEAgentic Systems – AG2 - AssessmentЗадание
12Agentic Systems – OpenAI Agents SDK8 материалов

Agentic Systems – OpenAI Agents SDK

Agents Introduction (101)ВидеоHandoff (Coding)ВидеоRunning Options (Coding)ВидеоGuardrails (Coding)ВидеоUsing Other Models (Coding)ВидеоTraces (Coding)ВидеоBuilding Agentic Workflows with OpenAI Agents SDKDIALOGUEAgentic Systems – OpenAI Agents SDK - AssessmentЗадание
13Agentic Systems – Google ADK5 материалов

Agentic Systems – Google ADK

ADK Introduction (101)ВидеоFunction Tools (Coding)ВидеоMulti Agents (Coding)ВидеоBuilding and Running Agents with Google ADKDIALOGUEAgentic Systems – Google ADK - AssessmentЗадание
14Agent Interactions (MCP, A2A, ACP)7 материалов

Agent Interactions (MCP, A2A, ACP)

Agent Interactions: MCP, A2A, and ACPВидеоModel Context Protocol (101)ВидеоMCP Client Installation (Coding)ВидеоMCP Server Setup (Coding)ВидеоMCP Server Testing (Coding)ВидеоUnderstanding Model Context Protocol (MCP) and Agent CommunicationDIALOGUEAgent Interactions (MCP, A2A, ACP) - AssessmentЗадание
15Model Finetuning5 материалов

Model Finetuning

Model Finetuning (101)ВидеоFinetuning with LoRA (101)ВидеоModel Finetuning - AssessmentЗаданиеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание