What skills will I gain from this course?

By the end of this course, you’ll understand how generative AI models work, how to write effective prompts, evaluate outputs, and apply AI in content creation, ideation, research assistance, and other professional tasks.

How long will it take to complete the course?

The course is designed to be completed in approximately 4–5 weeks with a recommended study pace of 4–5 hours per week. However, you can learn at your own pace, revisiting videos, readings, and quizzes as needed.

Do I need programming knowledge to learn Generative AI?

No, this course does not require any prior coding experience. All technical concepts are explained in a beginner-friendly manner, with visual examples.

What career opportunities can this course lead to?

Understanding generative AI opens doors to roles in content marketing, product innovation, research assistance, creative industries, and AI-related roles like prompt engineer, AI content curator, or digital strategist.

Will I receive a certificate upon completion?

Yes, you will receive a certificate of completion once you finish all the course modules and assessments. This certificate validates your understanding of Generative AI fundamentals and can strengthen your resume.

How is this course different from other AI programs?

Unlike highly technical AI programs, this course focuses on practical, beginner-level understanding and emphasizes real-world use cases of generative AI. It bridges the gap between theoretical AI concepts and everyday business or creative applications.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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

2,473 already enrolled

Included with Learn more

5 modules

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

5 modules

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

20 assignments¹

AI Graded see disclaimer

Taught in English

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Build your subject-matter expertise

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

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.

By the end of this course, you will be able to: - Define the core principles of generative AI, including models, algorithms, and applications. - Apply data pre-processing and vectorization techniques to enhance generative AI models. - Evaluate the strengths and weaknesses of GANs, autoencoders, transformers, and LLMs. - Analyze and optimize prompting techniques for improved model performance. - Design evaluation methods using metrics like BLEU and ROUGE to assess model outputs. This course is suitable for the aspiring AI practitioners, software developers, data scientists, and ML engineers who want to enhance their skills in building, deploying, and optimizing generative AI solutions. Join us to establish a solid foundation in generative AI and take your career to the next level with hands-on expertise in this transformative technology!

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 prompts

18 videosTotal 103 minutes

Specialization Introduction7 minutes
Course Introduction6 minutes
Defining Generative AI and its Evolution7 minutes
Generative Vs. Discriminative Models6 minutes
Applications of Generative AI5 minutes
Generative AI Pipeline Overview6 minutes
Importance of Data Pre-processing3 minutes
Navigating to Data Pre-processing5 minutes
Techniques for Data Cleaning7 minutes
Data Vectorization Methods7 minutes
Applications of Vectorization 5 minutes
Demonstration: Text Vectorization with TF-IDF and CountVectorizer7 minutes
Autoencoder Basics: Architecture and Training5 minutes
Variational Autoencoders: Overview and Applications6 minutes
Understanding Generative Adversarial Networks5 minutes
Demonstration: Building a Simple Autoencoder in Keras5 minutes
Training GANs: Key Considerations5 minutes
Variants of GANs: DCGAN, CycleGAN6 minutes

6 readingsTotal 90 minutes

Course Overview15 minutes
The Progression of AI Models: From Symbolic to Generative15 minutes
Vectorization Techniques: TF-IDF, Word2Vec, and Beyond15 minutes
Applications of VAEs in Real-World AI Problems15 minutes
GANs Explained: From Zero to DCGAN and CycleGAN15 minutes
Module Summary: Foundations of Generative AI15 minutes

4 assignmentsTotal 48 minutes

Practice Quiz: Understanding Generative AI6 minutes
Practice Quiz: Data Preparation and Vectorization6 minutes
Practice Quiz: Autoencoders and GANs6 minutes
Knowledge Check: Foundations of Generative AI30 minutes

3 discussion promptsTotal 15 minutes

Introduce Yourself5 minutes
Data Quality Vs. Model Complexity5 minutes
Latent Space in VAEs5 minutes

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 prompts

15 videosTotal 73 minutes

Evolution of Transformer Models5 minutes
Attention Mechanism and its Role in Transformers4 minutes
Demonstration: Visualizing Attention in Transformer Model6 minutes
Overview of BERT5 minutes
Overview of GPT4 minutes
Introduction to LLMs4 minutes
Exploring Meta Llama 3 and Other Leading LLMs5 minutes
Use Cases of LLMs in Various Domains6 minutes
Understanding LLM APIs and Their Types4 minutes
API Integration with LLMs5 minutes
Demonstration: LLM Integration with Gemini API6 minutes
Overview of Open-Source LLMs5 minutes
Ethical Considerations in Deploying Open-Source Models4 minutes
Comparing Open Source Models4 minutes
Demonstration: Hugging Face Ecosystem5 minutes

4 readingsTotal 60 minutes

From Attention to Transformers: How Sequence Modeling Evolved15 minutes
The Rise of Instruction-Tuned LLMs: A New Paradigm in NLP15 minutes
Ethical Risk Landscape in Open-Source AI Models15 minutes
Module Summary: Transformer Models and Large Language Models (LLMs)15 minutes

4 assignmentsTotal 48 minutes

Practice Quiz: Transformers and Attention Mechanism6 minutes
Practice Quiz: Exploring Large Language Models (LLMs)6 minutes
Practice Quiz: Open-Source LLM Ecosystem6 minutes
Knowledge Check: Transformer Models and Large Language Models (LLMs)30 minutes

3 discussion promptsTotal 15 minutes

GPT’s Generative Power5 minutes
Instruction-Tuned Models5 minutes
Responsible AI at Scale5 minutes

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 prompts

18 videosTotal 99 minutes

What is Prompt Engineering?6 minutes
Prompt Design Strategies for Generative AI Models5 minutes
Elements of an Effective Prompt5 minutes
Demonstration: Crafting Zero-Shot, One-Shot, and Few-Shot Prompts7 minutes
Demonstration: Designing Creative Prompts for Diverse NLP Tasks6 minutes
Few-shot and Zero-shot Learning Strategies5 minutes
Chain-of-Thought Prompting5 minutes
Optimizing Prompts for Specific Outputs6 minutes
Demonstration: Effective Prompt Design Strategies7 minutes
Introduction to Vector Databases4 minutes
ChromaDB, Pinecone, Weaviate5 minutes
Semantic Search with Vector Embeddings5 minutes
RAG Architecture and Retriever Techniques4 minutes
Modular and Multimodal RAG5 minutes
Demonstration: ChromaDB: Installing, Creating and Managing Vector Database5 minutes
Demonstration: Semantic Search with Vector Embeddings6 minutes
LangChain Architecture and Components5 minutes
Using Prompt Templates and Tools7 minutes

5 readingsTotal 75 minutes

The Science of Prompting: A Cognitive Perspective on Prompts15 minutes
How Vector Databases Power Semantic Search in AI15 minutes
RAG in the Wild: Case Studies from Enterprise Applications15 minutes
LangChain in Action: Building Modular AI Applications15 minutes
Module Summary: Generative AI Techniques and Tools15 minutes

5 assignmentsTotal 54 minutes

Practice Quiz: Principles of Prompt Engineering6 minutes
Practice Quiz: Advanced Prompting Techniques6 minutes
Practice Quiz: Vector Databases and RAG6 minutes
Practice Quiz: Frameworks for Application Development6 minutes
Knowledge Check: Generative AI Techniques and Tools30 minutes

4 discussion promptsTotal 20 minutes

Human Thinking, Machine Responses5 minutes
Prompting with Purpose5 minutes
Retrieval Meets Generation5 minutes
From Pieces to Products5 minutes

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 prompts

11 videosTotal 55 minutes

Fine-Tuning Fundamentals for Generative Models4 minutes
Data Augmentation and Hyperparameter Tuning6 minutes
Parameter-Efficient Fine-Tuning5 minutes
Low-Rank Adaptation4 minutes
Quantized Low-Rank Adaptation5 minutes
Challenges in Evaluating Generative AI Models4 minutes
Evaluation Metrics: BLEU, ROUGE, Inception Score4 minutes
Qualitative versus Quantitative Evaluation5 minutes
Demonstration: Evaluating Text Generation with BLEU and ROUGE7 minutes
Introduction to LLMOps5 minutes
Deployment Strategies for Generative AI Applications5 minutes

4 readingsTotal 60 minutes

Fine-Tuning vs Pretraining: When and Why to Choose Each15 minutes
Quantization in LLMs: Making Large Models Work on Small Devices15 minutes
The Emergence of LLMOps: Best Practices from Industry Leaders15 minutes
Module Summary: Fine-Tuning and Optimization of Generative Models15 minutes

5 assignmentsTotal 54 minutes

Practice Quiz: Fine-Tuning Techniques6 minutes
Practice Quiz: Advanced Prompting Techniques6 minutes
Practice Quiz: Evaluating Generative Models6 minutes
Practice Quiz: Building and Deploying Generative AI Solutions6 minutes
Knowledge Check: Fine-Tuning and Optimization of Generative Models30 minutes

4 discussion promptsTotal 20 minutes

Boosting with More Data5 minutes
Fine-Tuning in Tight Spaces5 minutes
Measuring the Immeasurable5 minutes
From Lab to Live5 minutes

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

1 videoTotal 3 minutes

Course Summary3 minutes

1 readingTotal 30 minutes

Practice Project: AI-Powered Content Strategy System30 minutes

2 assignmentsTotal 90 minutes

End Course Knowledge Check: Getting Started with Generative AI60 minutes
Designing a Generative AI-Based Text Generation and Application System30 minutes

1 discussion promptTotal 5 minutes

Describe Your Learning Journey5 minutes

2 ungraded labsTotal 60 minutes

Practice Activity: Text Vectorization and Autoencoder30 minutes
Practice Activity: BLEU and ROUGE Evaluation30 minutes

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Instructor

Edureka

245 Courses220,332 learners

Offered by

Edureka

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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.

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.