UcretsizAIbeginner

AI Engineering Essentials

Name: AI Engineering Essentials
Availability: InStock

This course is designed for aspiring AI engineers to gain a solid foundation in artificial intelligence. Step by step guide to becoming an AI Engineer. It covers essential introductory topics, core concepts, and practical tools that every beginner should know, helping you build the skills and confidence to start your AI journey.

8 haftaEN17 ders672 kayitli

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2 modul · 9 konu · 17 ders

AI PrimerA beginner-friendly course covering essential concepts and tools for aspiring AI engineers.

5 konu

FundamentalsNew Description

2 ders

Introduction to AI EngineeringThis module provides an overview of AI engineering, introducing key concepts, career pathways, and the fundamental skills required to design, build, and deploy AI systems.
Core AI TechnologiesThis module explores the foundational technologies behind AI, including Pre trained model, LLM and natural language processing, to build a strong technical base for AI engineering.

Pretrained Models & APIsNew Description

2 ders

Advanced AI Models and PlatformsThis module introduces pretrained AI models and popular platforms, showing how to leverage them for faster development, improved accuracy, and real-world applications.
Emerging AI Platforms and ToolsThis module highlights the latest AI platforms and tools shaping the industry, helping learners stay updated with cutting-edge technologies and practical applications.

API IntegrationNew Description

1 ders

OpenAI API MasteryThis module provides hands-on learning with the OpenAI API, teaching how to integrate, customize, and optimize AI models for real-world applications.

AI Ethics & Open source modelsNew Description

3 ders

AI Safety and EthicsThis module explores the principles of AI safety and ethics, focusing on responsible development, bias mitigation, and the societal impact of AI technologies
Open Source AI EcosystemThis module introduces the open-source AI ecosystem, covering popular frameworks, libraries, and communities that drive collaboration and innovation in AI development
Foundation ModelsThis module provides an exploration of foundation models, their architecture, training methods, and applications across domains like language, vision, and multimodal AI.

EmbeddingsNew Description

2 ders

Word Embeddings and Semantic SearchThis module explains how word embeddings capture semantic meaning in text and demonstrates their use in building intelligent search and information retrieval systems.
Embedding APIs and Model SelectionThis module explores embedding APIs and guides learners on selecting the right models for tasks such as semantic search, recommendation, and clustering.

Agent CoreVector Database, RAG, AI Agents & Multimodal

4 konu

Vector DatabaseNew Description

2 ders

Vector Database FundamentalsThis module introduces the fundamentals of vector databases, explaining how they store, index, and retrieve embeddings to power semantic search and AI-driven applications.
Vector Database PlatformsThis module explores leading vector database platforms such as Pinecone, Superbase, Chroma, FAISS, and Qdrant, focusing on their features, use cases, and integration with AI applications.

Retrieval-Augmented Generation (RAG)New Description

2 ders

RAG Fundamentals and StrategyThis module covers the core principles of Retrieval-Augmented Generation (RAG) and outlines strategies for designing effective RAG pipelines to enhance accuracy and reliability in AI applications.
RAG Implementation FrameworksThis module introduces popular frameworks for implementing Retrieval-Augmented Generation (RAG), such as LangChain and LlamaIndex, focusing on practical workflows and integration techniques

AI AgentsNew Description

1 ders

AI Agents FundamentalsThis module introduces the fundamentals of AI agents, explaining how they perceive, reason, and act to solve tasks autonomously using tools, memory, and decision-making strategies.

Multimodal AINew Description

2 ders

Multimodal AI ConceptsThis module explores the core concepts of multimodal AI, focusing on how models integrate and process multiple data types—such as text, images, audio, and video—to deliver richer insights and applications.
Visual AI and Image GenerationThis module introduces Visual AI techniques and image generation models, covering concepts like computer vision, diffusion models, and their applications in creating and understanding images.