Generative AI Fundamentals Specialization

Generative AI Fundamentals Specialization, available at $54.99, has an average rating of 3.4, with 9 lectures, 1 quizzes, based on 12 reviews, and has 2179 subscribers.

You will learn about Define Generative AI and its use-cases Learn about Large Language Models (LLM) Understand the LLMs in the AI landscape Get a grasp of Prompt Engineering Understand the Tools for Prompt Engineering Learn the fundamentals & importance of Responsible AI Understand Googles 7 AI principles Comprehend the impact, considerations, and ethical Issues of Generative AI This course is ideal for individuals who are Beginners and newbies aspiring for a career in Generative AI or Generative AI Scientists or AI Scientists & Engineers or Anyone interested in Artificial Intelligence and genAI or Generative AI Field Solutions Architect Managers or Generative AI Product Owners or Machine Learning Scientists or Machine Learning Engineers or Generative AI Specialists or Deep Learning Engineers or Data Scientists or Generative AI Leads or Generative AI Specialists or Data Science Managers or Data Pipeline Engineers or Data Engineers It is particularly useful for Beginners and newbies aspiring for a career in Generative AI or Generative AI Scientists or AI Scientists & Engineers or Anyone interested in Artificial Intelligence and genAI or Generative AI Field Solutions Architect Managers or Generative AI Product Owners or Machine Learning Scientists or Machine Learning Engineers or Generative AI Specialists or Deep Learning Engineers or Data Scientists or Generative AI Leads or Generative AI Specialists or Data Science Managers or Data Pipeline Engineers or Data Engineers.

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Summary

Title: Generative AI Fundamentals Specialization

Price: $54.99

Average Rating: 3.4

Number of Lectures: 9

Number of Quizzes: 1

Number of Published Lectures: 9

Number of Published Quizzes: 1

Number of Curriculum Items: 10

Number of Published Curriculum Objects: 10

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Define Generative AI and its use-cases

Learn about Large Language Models (LLM)

Understand the LLMs in the AI landscape

Get a grasp of Prompt Engineering

Understand the Tools for Prompt Engineering

Learn the fundamentals & importance of Responsible AI

Understand Googles 7 AI principles

Comprehend the impact, considerations, and ethical Issues of Generative AI

Who Should Attend

Beginners and newbies aspiring for a career in Generative AI

Generative AI Scientists

AI Scientists & Engineers

Anyone interested in Artificial Intelligence and genAI

Generative AI Field Solutions Architect Managers

Generative AI Product Owners

Machine Learning Scientists

Machine Learning Engineers

Generative AI Specialists

Deep Learning Engineers

Data Scientists

Generative AI Leads

Generative AI Specialists

Data Science Managers

Data Pipeline Engineers

Data Engineers

Target Audiences

Beginners and newbies aspiring for a career in Generative AI

Generative AI Scientists

AI Scientists & Engineers

Anyone interested in Artificial Intelligence and genAI

Generative AI Field Solutions Architect Managers

Generative AI Product Owners

Machine Learning Scientists

Machine Learning Engineers

Generative AI Specialists

Deep Learning Engineers

Data Scientists

Generative AI Leads

Generative AI Specialists

Data Science Managers

Data Pipeline Engineers

Data Engineers

A warm welcome to the Generative AI Fundamentals Specializationcourse by Uplatz.

Generative AI, also known as genAI, is a powerful and exciting field of artificial intelligence focused on creating new content, unlike many other AI systems that primarily analyze or interpret existing data. It can produce diverse outputs like:

Text: Poems, code, scripts, musical pieces, emails, letters, etc.

Images: Photorealistic portraits, landscapes, abstract art, 3D models, etc.

Audio: Music in various styles, sound effects, speech, etc.

Video: Realistic simulations, stylized animations, etc.

How Generative AI works

Imagine generative AI as a highly creative artist trained on massive amounts of data (text, images, etc.). This training allows it to:

1. Learn patterns and relationships within the data. For example, how words typically combine in sentences, how light interacts with objects to create an image, or how musical notes sequence to form melodies.

2. Develop statistical models that capture these patterns. These models act like internal “recipes” for generating new content.

3. Receive prompts or inputs from users, which guide the creative process. This could be a text description, a sketch, or even just a style preference.

4. Use its models and the provided prompts to generate entirely new creations that resemble the training data but are not simply copies.

Different techniques used in generative AI

Generative Adversarial Networks (GANs): Two AI models compete, one creating new content, the other trying to distinguish it from real data. This competition refines the generative model’s ability to create realistic outputs.

Variational Autoencoders (VAEs): Encode data into a latent space, allowing for manipulation and generation of new data points within that space.

Transformers: Powerful neural network architectures adept at understanding and generating text, code, and other sequential data.

Key points to remember

Generative AI is still under development, but it’s rapidly evolving with amazing potential.

While highly creative, it’s crucial to remember it’s still a machine and the ethical implications of its outputs need careful consideration.

It’s a powerful tool for various applications like art, design, drug discovery, and more.

Generative AI Fundamentals Specialization – Course Curriculum

Introduction to Generative AI

What is Generative AI?

Journey of Generative AI

How does Generative AI works?

Applications of generative AI in different sectors and industries

Introduction to Large Language Models (LLM)

What is LLM?

How do large language models work?

General Architecture of Large Language Model

What can a language model do?

What are the challenges and limitations of LLM?

LLM in the AI landscape

LLM use cases/Application

Generative AI: Prompt Engineering Basics

What is prompt Engineering?

Relevance of prompt engineering in generative AI models

Creating prompts and explore examples of impactful prompts

Commonly used tools for prompt engineering to aid with prompt engineering

Introduction to Responsible AI

What is Responsible AI?

Why it’s important?

How Google implements responsible AI in their products?

Google’s 7 AI principles

Generative AI: Impact, Considerations, and Ethical Issues

Limitations of generative AI and the related concerns

Identify the ethical issues, concerns, and misuses associated with generative AI

Considerations for the responsible use of generative AI

Economic and social impact of generative AI

Course Curriculum

Chapter 1: Introduction to Generative AI

Lecture 1: Part 1 – Introduction to Generative AI

Lecture 2: Part 2 – Introduction to Generative AI

Lecture 3: Part 3 – Introduction to Generative AI

Chapter 2: Introduction to Large Language Models (LLMs)

Lecture 1: Part 1 – Introduction to Large Language Models (LLMs)

Lecture 2: Part 2 – Introduction to Large Language Models (LLMs)

Chapter 3: Prompt Engineering

Lecture 1: Part 1 – Prompt Engineering

Lecture 2: Part 2 – Prompt Engineering

Chapter 4: Responsible AI

Lecture 1: Responsible AI

Chapter 5: Generative AI Impact, Considerations, and Ethical Issues

Lecture 1: Generative AI Impact, Considerations, and Ethical Issues

Chapter 6: End of Course Quiz

Instructors

Uplatz Training
Fastest growing global Technology & Cloud Training Provider

Rating Distribution

1 stars: 2 votes

2 stars: 3 votes

3 stars: 1 votes

4 stars: 1 votes

5 stars: 5 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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