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This course introduces the foundational concepts and advanced techniques in Generative AI, covering key topics such as model architectures, data preparation, prompt engineering, and deployment strategies. Learners will gain practical experience with cutting-edge tools and methodologies to effectively design, fine-tune, and deploy generative AI solutions.
Getting Started with Generative AI
您将学到什么
Define generative AI principles and apply data preparation, vectorization, and model-building techniques.
Analyze and compare models like GANs, VAEs, transformers, and LLMs for practical applications.
Design effective prompts using few-shot, zero-shot, and chain-of-thought techniques for AI models.
Optimize and deploy generative AI models using fine-tuning, PEFT, and LLMOps strategies.
您将获得的技能
- Deep Learning
- Prompt Engineering
- Open Source Technology
- Data Cleansing
- Artificial Intelligence and Machine Learning (AI/ML)
- OpenAI
- Generative Model Architectures
- AI Personalization
- Application Deployment
- Data Processing
- Database Systems
- Machine Learning
- Responsible AI
- Feature Engineering
- Data Visualization
- Generative AI
- Natural Language Processing
- Large Language Modeling
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 Generative AI for Software Engineers & Developers 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有5个模块
This module introduces the fundamentals and advanced concepts of Generative AI, including its evolution, real-world applications, and key differences from discriminative models. Learners will explore data preprocessing, vectorization techniques like TF-IDF and Word2Vec, and gain hands-on experience with Autoencoders and GANs, enabling them to build and train generative models for AI-driven solutions.
涵盖的内容
18个视频6篇阅读材料4个作业3个讨论话题3个插件
This module covers the fundamentals of attention mechanisms, the evolution of transformers, and major LLMs like GPT, PaLM, and LLaMA. It includes instruction-tuned models, API integration, and real-world applications. You’ll also explore the open-source LLM ecosystem, model comparisons, Hugging Face, and key ethical considerations.
涵盖的内容
15个视频4篇阅读材料4个作业3个讨论话题1个插件
This module covers prompt engineering essentials, advanced prompting techniques like few-shot, zero-shot, and chain-of-thought, and strategies for optimizing generative AI outputs. You’ll learn how vector databases (ChromaDB, Pinecone, and Weaviate) enable semantic search and Retrieval-Augmented Generation (RAG). Hands-on work with LangChain shows how to build modular AI apps using prompt templates, tools, and agents for practical, state-of-the-art solutions.
涵盖的内容
18个视频5篇阅读材料5个作业4个讨论话题1个插件
This module covers fine-tuning and optimizing generative models, including basics like data augmentation and hyperparameter tuning, and advanced methods such as PEFT, LoRA, and QLoRA for efficient adaptation. You’ll learn how to evaluate models using metrics like BLEU and ROUGE, balancing quantitative and qualitative assessments. The course also introduces building and deploying AI solutions with LLMOps and industry best practices for real-world use.
涵盖的内容
11个视频4篇阅读材料5个作业4个讨论话题1个插件
This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz, project, and labs.
涵盖的内容
1个视频1篇阅读材料2个作业1个讨论话题2个非评分实验室
获得职业证书
将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。
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常见问题
This course is ideal for beginners, professionals from technical backgrounds, and anyone curious about how AI can be used to generate content. No prior AI or coding experience is required to get started.
The course introduces key concepts such as how generative AI works, the types of models used (e.g., transformers, GANs), real-world applications, ethical considerations, prompt engineering, and hands-on demos with generative AI techniques.
The course features hands-on demonstrations and practice exercises with real generative AI tools and platforms, guiding you through model interactions using prompts and exploring practical applications like text summarization and more.
更多问题
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¹ 本课程的部分作业采用 AI 评分。对于这些作业，将根据 Coursera 隐私声明使用您的数据。