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Generative AI Essentials
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Discover AI fundamentals, history, models, and ethics in our generative AI course—gain the AI skills to innovate and lead in an AI-driven future.

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Overview

Generative AI transforms industries, drives innovation, and unlocks new possibilities across sectors. This course provides a deep understanding of generative AI models and their applications. You’ll start by exploring the fundamentals of generative AI and how these technologies offer groundbreaking solutions to contemporary challenges. You’ll delve into the building blocks, including the history of generative AI, language vectorization, and creating context with neuron-based models. As you progress, you’ll...Show More

Generative AI transforms industries, drives innovation, and unlocks new possibilities across sectors. This course provides a dee...Show More

WHAT YOU'LL LEARN

An understanding of core concepts, foundational techniques, and innovative applications of generative AI

Proficiency in preprocessing text data and understanding the evolution of natural language processing (NLP)

The ability to use sequence models and attention mechanisms to build and understand context within language data

An understanding of the importance of foundation models and knowledge of different pretraining and fine-tuning paradigms

Practical knowledge of vision and audio generation to create comprehensive multimodal models

An understanding of the ethical considerations, regulations, and future trends in the development and systems of generative AI

An understanding of core concepts, foundational techniques, and innovative applications of generative AI

TAKEAWAY SKILLS

Generative AI

Natural Language Processing

Large Language Models (LLMs)

Content

33 Lessons9 Quizzes

Introduction to Generative AI

2 Lessons

Explore the fundamentals and applications of generative AI for innovative solutions.

Why Learn Generative AI?

What Is Generative AI?

Building Blocks of Generative AI

11 Lessons

Explore the evolution and techniques of generative AI and modern NLP.

Text Preprocessing Essentials

The Emergence of NLP

Vectorizing Language

Building Context with Neurons

Reconstructing Context with Sequence Models

Learning Phrase Representations Using Encoder-Decoder

Emergence of Generative AI

Attention Is All You Need

Bidirectional Transformers for Language Understanding

Improving Language Understanding by Generative Pretraining

Evaluating Large Language Models

Foundation Models

10 Lessons

Explore foundation models, pretraining, optimization, and multimodal capabilities in generative AI.

What Are Foundation Models?

How Do Models Learn?

Pretraining Paradigms

Post-Training, Fine-Tuning, and Adaptation

Model Optimization for Deployment

Large Language Models: Language at a Scale

Understanding Vision Models: How AI Learns to See

AI Image Generation: Diving into Diffusion Models

Audio Generation

Multimodal Models in Generative AI

Generating New Music with Artificial Intelligence

Project

Intelligent Interaction with GenAI

3 Lessons

Master effective AI communication, dynamic knowledge integration, and autonomous agent capabilities.

Communicating with AI

Bridging Static Knowledge and Dynamic Context in AI

Autonomous AI Agents

Practical Applications and Case Studies

5 Lessons

Explore the transformative applications of generative AI across various media formats.

Applying Fundamental Concepts to Real-World Systems

Text-to-Text Generation Systems

Text-to-Image Generation Systems

Text-to-Speech Generation Systems

Text-to-Video Generation Systems

Future of Generative AI and Wrap Up

2 Lessons

Explore the transformative potential and ethical considerations of generative AI advancements.

The Future of Generative AI: Trends and Challenges

The Way Forward

Exploring Public Sentiment on Twitter with NLP

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

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Frequently Asked Questions

What are the core concepts of generative AI?

Core concepts of generative AI involve neural networks, adversarial training (GANs), variational inference (VAEs), and the ability of models to learn and generate data that resembles the training data distribution.

What is generative AI?

Generative AI refers to a class of artificial intelligence algorithms that can generate new content, such as text, images, music, and videos, by learning patterns from existing data.

Is generative AI difficult to learn?

Generative AI can be challenging to learn due to its complex mathematical and computational foundations, requiring knowledge of machine learning, neural networks, and programming skills, but introductory resources are available.

What is generative AI in simple words?

In simple words, generative AI is like teaching a computer to create new things, like writing stories or drawing pictures, by showing it lots of examples and letting it learn how to make its own versions.

What are GenAI basics?

The basics of GenAI include understanding models like GANs and VAEs, the concepts of latent space and training data, and how these models learn to generate new, realistic data instances.

Generative AI Essentials Save

Generative AI Essentials
Save