What is the Generative AI Foundations course about?

The Generative AI Foundations course is designed to introduce learners to the fundamentals of generative artificial intelligence. The course covers a wide range of topics, including the principles of generative AI, code generation with ChatGPT, prompt engineering, leveraging ChatGPT for learning Python and software development, autoencoders, GANs (Generative Adversarial Networks), language models, and transformer-based generative models. Through videos, readings, and practical assignments, learners will gain a comprehensive understanding of generative AI technologies and their applications.

Who should enroll in this course?

This course is ideal for anyone interested in understanding and working with generative AI technologies, including software developers, data scientists, researchers, and students in computer science or related fields. Prior knowledge of Python and basic concepts of machine learning will be helpful but not mandatory.

How is the course content delivered?

The course content is delivered through a mix of instructional videos, reading materials, and practice assignments. Each lesson includes videos that cover key topics, readings to deepen your understanding, and practical assignments to apply what you've learned. There are also discussion prompts to encourage interaction among students. The course content is delivered through a mix of instructional videos, reading materials, and practice assignments. Each lesson includes videos that cover key topics, readings to deepen your understanding, and practical assignments to apply what you've learned. There are also discussion prompts to encourage interaction among students.

Are there any graded assignments or assessments?

Yes, the course includes both practice assignments and graded assignments. Practice assignments are designed to reinforce learning and allow students to apply concepts in practical scenarios. Graded assignments are used to assess understanding of the course material, and you must complete these assignments to earn a certificate of completion.

How can I apply the knowledge gained from this course?

The knowledge gained from this course can be applied in various domains such as software development, data science, content generation, image and video generation, enhancing creativity in design, and solving complex computational problems with generative models. Additionally, the skills learned can be utilized in academic research and industry projects focused on AI and machine learning.

Will I receive a certificate upon completing the course?

Yes, upon successfully completing the course requirements and passing the graded assignments, you will receive a certificate of completion, demonstrating your knowledge and skills in generative AI foundations.

How long will it take to complete the course?

The duration to complete the course will vary depending on the individual's pace of learning and the time dedicated to studying and completing assignments. However, the course is designed to be comprehensive yet flexible to accommodate different learning speeds.

What types of generative models (e.g., GANs, VAEs, transformers) will I build or experiment with?

You’ll be introduced to models such as GANs, VAEs, and transformers, with guided exercises to explore how they generate text, images, and other data.

What level of math or probability background is required for this course?

Only a basic understanding of linear algebra and probability is expected, making the course accessible to learners from diverse backgrounds.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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.

Generative AI Foundations

This course is part of Learn Generative AI with LLMs Specialization

Instructor: Edureka

3,045 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

17 reviews

Beginner level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

17 reviews

Beginner level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Gain a comprehensive overview of generative AI and its core techniques.
Learn how to apply generative AI for tasks in text, image, and code generation.
Build practical experience with models and interactive AI platforms.
Understand ethical, mathematical, and foundational principles of generative AI.

Skills you'll gain

Generative Model Architectures

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments

Taught in English

91% of learners achieved a positive career outcome

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Build your subject-matter expertise

This course is part of the Learn Generative AI with LLMs 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 4 modules in this course

Generative AI Foundations is a comprehensive course designed to provide learners with a strong foundation in Generative Artificial Intelligence, covering key principles, core methodologies, and real-world applications across multiple domains such as text, images, audio, and code. Ideal for beginners and professionals alike, this course explores how Generative AI models like GANs, VAEs, and transformers are transforming industries through content creation, automation, and innovation.

By the end of this course, you will have acquired the knowledge and skills to: - Grasp the foundational concepts and technical intricacies of Generative AI, including its advantages and limitations. - Apply Generative AI for code generation, enhancing your programming efficiency and creativity in Python and other languages. - Master the art of prompt engineering to optimize interactions with AI models like ChatGPT, leading to improved outcomes in code generation and beyond. - Utilize ChatGPT for learning and mastering Python, data science, and software development practices, thereby broadening your technical skill set. - Explore the revolutionary fields of Autoencoders and Generative Adversarial Networks (GANs), understanding their architecture, operation, and applications. - Dive into the world of language models and transformer-based generative models, gaining insights into their mechanisms, applications, and impact on the future of AI. This course is meticulously crafted to cater to a broad audience, including software developers, data scientists, AI enthusiasts, and professionals seeking to leverage Generative AI technologies for innovative solutions. While prior knowledge of Generative AI Fundamentals or Python Coding is helpful, but it is not a prerequisite to complete the course. Whether you're looking to enhance your existing skills or embark on a new career path in the field of AI, this course will provide you with the knowledge, practical skills, and confidence to succeed. Join us on this exciting journey into the world of Generative AI!

Module details

This module is designed to equip learners with a solid understanding of Generative AI principles, models, and applications, setting the stage for more advanced exploration. Through engaging lessons that include videos on the overview of Generative AI, its principles, understanding its models, and the advantages and disadvantages, along with practical applications like code generation and prompt engineering, participants will gain valuable insights. This module also emphasizes ethical considerations and includes practice assignments and discussion prompts to encourage active learning and application of concepts. Whether you're new to AI or looking to enhance your understanding of Generative AI's capabilities, this module provides the essential knowledge base to start your journey.

What's included

16 videos6 readings4 assignments3 discussion prompts

16 videosTotal 85 minutes

Course Introduction4 minutes
Overview of Generative AI8 minutes
Generative AI Principles2 minutes
Generative AI vs. Generative AI Model6 minutes
Understanding Generative AI Models7 minutes
Transformer - Based , Energy - Based & Conditional Generation Models3 minutes
Generative AI for Code Generation4 minutes
Benefits of Generative AI for Code Generation5 minutes
Introduction to ChatGPT4 minutes
Log-in Process5 minutes
Code Generation with ChatGPT6 minutes
Leveraging ChatGPT to Learn Data Science with Python7 minutes
Visualization with Gen AI7 minutes
Exploratory Data Analysis5 minutes
Demonstration on Exploratory Data Analysis6 minutes
Exploratory Data Analysis with the help of ChatGPT8 minutes

6 readingsTotal 50 minutes

Course Overview5 minutes
Ethical Considerations in Generative AI: A Guide for Responsible Innovation10 minutes
How to use Discussion Forums5 minutes
ChatGPT Account Creation10 minutes
How Python Professionals can use ChatGPT10 minutes
Generative AI: Foundations, Applications, and Ethical Exploration10 minutes

4 assignmentsTotal 38 minutes

Knowledge Check: Generative AI Getting Started6 minutes
Knowledge Check: Prompt Engineering Fundamentals6 minutes
Knowledge Check: Leveraging ChatGPT by Software Developers6 minutes
Knowledge Check: Generative AI-Getting Started20 minutes

3 discussion promptsTotal 30 minutes

Balancing Innovation and Ethical Responsibility in Generative AI Applications10 minutes
Optimize Prompt Design10 minutes
Python Development Workflow 10 minutes

This module is crafted to provide an in-depth understanding of how these models function, their architectural nuances, and their wide array of applications in the tech industry. Starting with the basics of Autoencoders, learners will explore the workings and variations of these networks, including Variational Autoencoders (VAEs), and understand their significance in data compression and generative tasks. The journey continues with an exploration of GANs, from their foundational architecture to the nuances of training and the exploration of their diverse variants. Through practical assignments, engaging video content, and focused readings, participants will gain hands-on experience working with these models, culminating in a deeper comprehension of their capabilities and limitations.

What's included

10 videos3 readings4 assignments3 discussion prompts

10 videosTotal 57 minutes

Working of Autoencoders10 minutes
Variational Autoencoders6 minutes
Introduction to GAN6 minutes
Working of GAN4 minutes
Basic GAN Architecture6 minutes
Variants of GANs6 minutes
BigGAN2 minutes
Training GANs7 minutes
About GAN4 minutes
Data Compression with Autoencoders7 minutes

3 readingsTotal 30 minutes

Variational Autoencoders: Applications and Insights10 minutes
Technical Symphony of Variational Autoencoders in Data Compression10 minutes
Summary and Consolidation of Autoencoders and GANs10 minutes

4 assignmentsTotal 38 minutes

Knowledge Check: Basic Autoencoders6 minutes
Knowledge Check: GAN Architecture6 minutes
Knowledge Check: GAN Practicals6 minutes
Knowledge Check: Autoencoders and GANs20 minutes

3 discussion promptsTotal 30 minutes

Transforming Industries with Autoencoders and VAEs: Benefits and Challenges10 minutes
Adversarial Dynamics in GANs10 minutes
Overcoming GAN Challenges for Enhanced Creativity and Realism10 minutes

This module provides an in-depth exploration of Language Models and Transformer-based Generative Models, foundational elements in natural language processing and artificial intelligence. Starting with an overview of language models, it progresses to cover the revolutionary transformer architecture, detailing its attention mechanism and various advanced models. The module then shifts focus to groundbreaking models such as GPT and BERT, examining their development, capabilities, and the wide array of applications they enable in the AI domain. Concluding with comprehensive assessments, including practice and graded assignments on cutting-edge topics like VAEs and GANs, the module offers a holistic understanding of how these technologies drive innovation in AI research and applications.

What's included

9 videos4 readings3 assignments

9 videosTotal 47 minutes

Exploring Language Models6 minutes
Types of Language Models6 minutes
Transfer Models5 minutes
Applications of Language Models7 minutes
Summarization and Search2 minutes
Introduction to GPT5 minutes
Understanding GPT5 minutes
BERT6 minutes
Inference in BERT4 minutes

4 readingsTotal 40 minutes

The Transformer Architecture: Attention Mechanism10 minutes
Advanced Transformer Models10 minutes
Applications of Transformer Based Models10 minutes
Module Summary: Exploring Language Models and Transformer-Based Generative Models10 minutes

3 assignmentsTotal 32 minutes

Knowledge Check: Language Models6 minutes
Knowledge Check: GPT and BERT6 minutes
Knowledge Check: Language Models and Transformer-based Generative Models20 minutes

This final module is designed to consolidate the knowledge and skills learners have acquired throughout the course. It starts with a Practice Project, encouraging learners to apply their understanding in a hands-on manner, thus bridging the gap between theoretical knowledge and practical application. Following this, the module offers a Graded Assignment on Gen AI Fundamentals, aimed at rigorously evaluating the learners' grasp of the key concepts, techniques, and applications explored in the course.