Generative AI Foundations

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Generative AI Foundations

This course is part of Learn Generative AI with LLMs Specialization

Instructor: Edureka

Included with Coursera Plus

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4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

8 hours to complete

3 weeks at 2 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

8 hours to complete

3 weeks at 2 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Master Generative AI concepts, apply them in code generation and gain expertise in advanced models like Autoencoders and GANs.

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

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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
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There are 4 modules in this course

Welcome to the "Generative AI Foundations" course, a learning journey designed to equip you with a deep understanding of Generative AI, its principles, methodologies, and applications across various domains.

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!

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 minutesPreview module
Overview of Generative AI7 minutes
Generative AI Principles2 minutes
Generative AI vs. Generative AI Model5 minutes
Understanding Generative AI Models6 minutes
Transformer - Based , Energy - Based & Conditional Generation Models3 minutes
Generative AI for Code Generation3 minutes
Benefits of Generative AI for Code Generation4 minutes
Introduction to ChatGPT3 minutes
Log-in Process4 minutes
Code Generation with ChatGPT5 minutes
Leveraging ChatGPT to Learn Data Science with Python6 minutes
Visualization with Gen AI7 minutes
Exploratory Data Analysis4 minutes
Demonstration on Exploratory Data Analysis6 minutes
Exploratory Data Analysis with the help of ChatGPT7 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 29 minutes

Knowledge Check: Generative AI-Getting Started20 minutes
Knowledge Check: Generative AI Getting Started3 minutes
Knowledge Check: Prompt Engineering Fundamentals3 minutes
Knowledge Check: Leveraging ChatGPT by Software Developers3 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 minutesPreview module
Variational Autoencoders6 minutes
Introduction to GAN5 minutes
Working of GAN3 minutes
Basic GAN Architecture5 minutes
Variants of GANs6 minutes
BigGAN2 minutes
Training GANs6 minutes
About GAN3 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 29 minutes

Knowledge Check: Autoencoders and GANs20 minutes
Knowledge Check: Basic Autoencoders3 minutes
Knowledge Check: GAN Architecture3 minutes
Knowledge Check: GAN Practicals3 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 46 minutes

Exploring Language Models5 minutesPreview module
Types of Language Models6 minutes
Transfer Models5 minutes
Applications of Language Models7 minutes
Summarization and Search2 minutes
Introduction to GPT4 minutes
Understanding GPT4 minutes
BERT5 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 26 minutes

Knowledge Check: Language Models and Transformer-based Generative Models20 minutes
Knowledge Check: Language Models3 minutes
Knowledge Check: GPT and BERT3 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.

What's included

1 video2 readings1 assignment

Instructor

Edureka

47 Courses41,960 learners

Offered by

Edureka

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Frequently asked questions

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.

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.

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.

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.

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. 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.

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. If you only want to read and view the course content, you can audit the course for free.

Generative AI Foundations

What you'll learn

Details to know

See how employees at top companies are mastering in-demand skills

Build your subject-matter expertise

Earn a career certificate

There are 4 modules in this course

Gen AI Foundations

What's included

Autoencoders and GANs

What's included

Language Models and Transformer-based Generative Models

What's included

Course Wrap-up and Assessment

What's included

Instructor

Offered by

Recommended if you're interested in Machine Learning

Introduction to Generative AI - 简体中文

Introduction to Generative AI - 繁體中文

Prompt Engineering

Generative AI Essentials: Overview and Impact

Why people choose Coursera for their career

New to Machine Learning? Start here.

Open new doors with Coursera Plus

Advance your career with an online degree

Join over 3,400 global companies that choose Coursera for Business

Frequently asked questions

What is the Generative AI Foundations course about?

Who should enroll in this course?

How is the course content delivered?

More questions