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
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Getting Started with Generative AI
This course is part of Generative AI for Software Engineers & Developers Specialization
Instructor: Edureka
Included with 
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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.
Skills you'll gain
- Scalability
- Data Visualization
- Deep Learning
- MLOps (Machine Learning Operations)
- Generative AI
- Application Deployment
- Natural Language Processing
- OpenAI
- Open Source Technology
- Prompt Engineering
- Data Ethics
- Database Systems
- Data Processing
- Artificial Intelligence and Machine Learning (AI/ML)
- Feature Engineering
- Data Cleansing
- AI Personalization
- Large Language Modeling
- Artificial Intelligence
- Machine Learning
Details to know
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Recently updated!
August 2025
Taught in English
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This course is part of the Generative AI for Software Engineers & Developers Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Gain a foundational understanding of a subject or tool
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There are 5 modules in this course
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.
What's included
18 videos6 readings4 assignments3 discussion prompts3 plugins
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.
What's included
15 videos4 readings4 assignments3 discussion prompts1 plugin
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.
What's included
18 videos5 readings5 assignments4 discussion prompts1 plugin
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.
What's included
11 videos4 readings5 assignments4 discussion prompts1 plugin
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.
What's included
1 video1 reading2 assignments1 discussion prompt2 ungraded labs
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Frequently asked questions
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.
More questions
Financial aid available,
¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.