Category: Tutorials

Last updated 4/2024 Created by Pixovert Studio MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 15 Lectures ( 2h 58m ) | Size: 2.6 GB​

What you’ll learn
Master the art of Prompt Engineering to Provide Professional, reliably consistent results
Create Huge Landscapes using built-in features in Comfy-UI – for SDXL or earlier versions of Stable Diffusion
Produce beautiful portraits in SDXL
Use the Speed and Efficiency of ComfyUI to do batch processing for more effective cherry picking
Understand the dualism of the Classifier Free Guidance and how it affects outputs

Requirements
The ability to setup and run Stable Diffusion through the ComfyUI interface
A powerful consumer PC or Professional Workstation with a Stable Diffusion capable GPU
The desire to achieve reliably professional diffusions
Attention to detail. Attention to Process
Genuine interest and skill in critiquing and evaluativing Generative AI outputs

Description
Do you already know how to create stunning images from text prompts? Have you have started to unleash your creativity and produced amazing art using the power of artificial intelligence? Do you want to go the next step and step up to consistently producing professional results? If so, then this course is for you!This course is designed for professionals who already have some experience with text-to-image synthesis and want to take their skills to the next level. You will learn how to master Stable Diffusion, the state-of-the-art latent text-to-image diffusion model that can generate photo-realistic images given any text input. You will also learn how to master SDXL, the latest and most advanced version of Stable Diffusion, which can handle challenging concepts and produce images of high quality in virtually any art style. And you will learn how to master ComfyUI, the robust and modular Stable Diffusion GUI and backend that enables you to design and execute advanced Stable Diffusion pipelines using a graph and nodes-based interface.By taking this course, you will also be able to fine-tune the parameters and the workflows to your needs and preferences, as well as write creative and high-quality prompts that can produce amazing results. You will also gain a deeper understanding of the underlying principles and techniques of text-to-image synthesis, latent diffusion models, and image refinement techniques.This course is not for beginners who need introductory learning. This course is for professionals who want to master Stable Diffusion, SDXL and ComfyUI and achieve polished, professional outcomes. If you are ready to take your text-to-image synthesis skills to the next level, then enroll in this course today!Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. It is based on the idea of reversing the diffusion process, which gradually transforms an image into random noise. By applying the reverse steps with a neural network conditioned on text, Stable Diffusion can recover the original image from the noise.SDXL is the latest and most advanced version of Stable Diffusion, which leverages a larger and more powerful UNet backbone with more attention blocks and a larger cross-attention context. SDXL can generate images of high quality in virtually any art style and is the best open model for photorealism. It can also handle challenging concepts such as hands, text, and spatial arrangements.ComfyUI supports SD1.x, SD2.x and SDXL models, as well as standalone VAEs and CLIP models. ComfyUI also offers many features such as embeddings, textual inversion, Loras, hypernetworks, loading and saving workflows, and image control.The goal of this course is to help you achieve polished, professional outcomes with Stable Diffusion, SDXL and ComfyUI. You will learn how to use these tools effectively and efficiently, as well as how to fine-tune them to the demands of your work and your preferences.  By the end of this course, you will be able to get the most out of the efficiency and flexibility of ComfyUI to produce reliably consistent results that begin to match the best in the industry.

Who this course is for
Advanced ComfyUI users with a real desire to improve the speed, efficiency and quality of diffusions
Professionals working in Generative AI who want to understand the benefits of ComfyUI and SDXL
ComfyUI users who want to increase their repertoire of methods and processes

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Published 3/2026 MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 5h 1m | Size: 3.18 GB​

The Complete Story of AI: Neural Networks, Deep Learning & LLMs

What you’ll learn
Clear theoretical foundations of artificial intelligence and its main concepts.
A structured understanding of how different AI techniques work at a conceptual level.
Key terminology and core principles used in modern AI systems.
Theoretical insight into how AI models are designed, evaluated, and improved.
An overview of common AI applications and how they operate behind the scenes.
A solid conceptual base in AI that supports future independent study and professional development.

Requirements
There is no request.

Description
This course contains the use of artificial intelligence.

Discover how generative AI and ChatGPT came to be-from the first mathematical neurons to large language models. A complete, story-driven history of artificial intelligence.

This course walks you through the full arc of AI: the ideas that turned thought into calculation, the first "learning" machines, the winters and booms, and the breakthroughs that led to GPT, DALL-E, and today’s generative AI. No heavy math or coding-just clear explanations, timelines, and the key people and papers that built the field.

What you’ll learn

Where AI really started – From Leibniz and Boolean logic to the first formal models of computation and the neuron

The birth of "artificial intelligence" – Dartmouth, early neural networks, ELIZA, and the first AI winters

Expert systems and the return of neural nets – Rule-based AI, backpropagation, and Deep Blue

The deep learning revolution – ImageNet, AlexNet, GANs, AlphaGo, and the transformer architecture

Generative AI and LLMs – How GPT, diffusion models, and tools like ChatGPT and DALL-E emerged from this history

Why take this course?

Story-first: One continuous narrative from the 1840s to the present, so you see how each step led to the next.

Built for SEO and clarity: Covers the history of AI, generative AI, machine learning, neural networks, deep learning, ChatGPT, and LLMs in one place.

No prerequisites: Designed for curious learners, professionals, and anyone who wants to understand where ChatGPT and generative AI came from.

-friendly length: Bite-sized lessons (under 6 minutes each) so you can learn at your own pace.

Who this is for

Anyone curious about the history of artificial intelligence and generative AI

Professionals who want context on ChatGPT, large language models, and AI tools

Students and lifelong learners interested in machine learning, neural networks, and deep learning from a historical angle

By the end, you’ll have a clear mental map of how we went from "thought as calculation" to the AI systems we use every day-and you’ll be able to explain that story to others.

Enroll now and learn the full history of AI and generative AI in one structured, engaging course.

Who this course is for
This course is designed for students, professionals, and enthusiasts who want a clear theoretical introduction to artificial intelligence without focusing on programming or hands-on implementation.

Generative AI Hallucinations and Retrieval Reliability
.MP4, AVC, 1920×1080, 30 fps | English, AAC, 2 Ch | 35m | 191 MB
Instructor: Praveenkumar Bouna​

Diagnose and fix hallucinations in LLM applications. This course teaches you to identify root causes, implement mitigation strategies, and improve retrieval reliability for production deployments.

What you’ll learn

Hallucinations are one of the most common failure modes in generative AI systems, causing incorrect information, fabricated details, and unreliable outputs in LLM applications. In this course, Generative AI Hallucinations and Retrieval Reliability, you’ll learn to build robust systems that deliver accurate, reliable responses.

First, you’ll explore how to diagnose hallucinations by identifying root causes across prompting and grounding components in LLM applications, then learn mitigation strategies. Next, you’ll discover how to improve prompting practices by identifying anti-patterns and replacing them with structured, reproducible approaches. Finally, you’ll learn how to improve retrieval reliability in LLM applications.

When you’re finished with this course, you’ll have the skills and knowledge needed to build LLM applications that consistently produce accurate, reliable outputs.

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Generative AI for Web Developers
ISBN: 0135375339 | .MP4, AVC, 1280×720, 30 fps | English, AAC, 2 Ch | 3h 15m | 991 MB
Instructor: Shaun Wassell​

Sneak Peek

The Sneak Peek program provides early access to Pearson video products and is exclusively available to subscribers. Content for titles in this program is made available throughout the development cycle, so products may not be complete, edited, or finalized, including video post-production editing.

Introduction

Generative AI for Web Developers: Introduction

Lesson 1: Generative AI Options for Web Development

Learning objectives
1.1 Set up and work with ChatGPT
1.2 Learn how to interact with ChatGPT effectively
1.3 Get started with Github Copilot
1.4 Learn best practices for navigating Github Copilot
1.5 Set up and Work with Google Gemini
1.6 Learn how to interact with Google Gemini effectively

Lesson 2: Front-End and UI Development with Generative AI

Learning objectives
2.1 Create and design user interfaces with AI
2.2 Translate between frameworks: React, Angular, Vue, and more
2.3 Write AI-generated tests for the front-end
2.4 Generate text and image content with AI
2.5 Use AI to improve website accessibility

Lesson 3: Server Development with Generative AI

Learning objectives
3.1 Create and use back-end test data
3.2 Create Basic Servers with Generative AI
3.3 Identify potential security vulnerabilities
3.4 Write server tests with Generative AI

Lesson 4: Working with Databases with Generative AI

Learning objectives
4.1 Create schemas with AI
4.2 Use Generative AI to help with queries
4.3 Optimize and index databases with AI

Summary

Generative AI for Web Developers: Summary
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Generative AI for Mentors and Coaches
Duration: 14m | .MP4 1280×720, 30 fps(r) | AAC, 48000 Hz, 2ch | 239 MB
Genre: eLearning | Language: English​

The process of helping others achieve their goals is rewarding. This course will teach you how to leverage GenAI to help with providing feedback, goal setting, producing skill improvement plans, and drafting emails for your clients.
Although rewarding, coaching and mentoring others can involve a lot of repetitive tasks, like offering feedback and goal-setting advice. In this course, Generative AI for Mentors and Coaches, you’ll learn to leverage generative AI to help with these tasks. First, you’ll explore creating helpful client feedback using GenAI. Next, you’ll learn how to help clients define clear, realistic, and achievable goals. Finally, you’ll use GenAI to create skill improvement plans and emails.
When you’re finished with this course, you’ll have the skills and knowledge of GenAI needed to make coaching and mentoring easier.

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Generative AI for Interviewers
.MP4, AVC, 1280×720, 30 fps | English, AAC, 2 Ch | 19m | 74.2 MB
Instructor: Alice Meredith​

This course will help you streamline and enhance your hiring process using AI.

What you’ll learn

Recruiting the right talent is becoming increasingly complex, with larger pools of applicants and the need for diverse and inclusive hiring practices. In this course, Generative AI for Interviewers, you’ll explore how AI can elevate your interviewing process.

First, you’ll see how to create effective job descriptions and inclusive and fair interview guides using AI tools. Next, you’ll explore how different AI tools that automate and improve resume summarizing, allowing you to identify the most suitable candidates quickly. Finally, you’ll learn how to use AI tools to capture critical notes from interviews accurately.

When you’re finished with this course, you’ll have increased AI skills and knowledge to efficiently identify and engage top talent while maintaining high standards of equity.

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Generative AI for Innovators
.MP4, AVC, 1920×1080, 30 fps | English, AAC, 2 Ch | 19m | 115 MB
Instructor: Esteban Herrera​

Generative AI tools can dramatically accelerate product innovation. This course will teach you how to harness these tools to create new products and solutions.

What you’ll learn

Innovators often struggle to make informed decisions quickly in today’s rapidly evolving technology landscape.

In this course, Generative AI for Innovators, you’ll learn to use Generative AI-powered tools to accelerate your research and innovation processes. First, you’ll explore how to conduct technology landscape research. Next, you’ll discover how to transform research findings into actionable strategies by leveraging AI’s analytical capabilities. Finally, you’ll learn how to design and structure experiments to validate your innovations.

When you’re finished with this course, you’ll have the skills and knowledge of Generative AI-powered tools needed to drive innovation initiatives efficiently.

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GenAI Model Access Layer and Structured Outputs
Last Updated Feb 10, 2026
Duration: 1h 56m | .MP4 1920×1080 30 fps(r) | AAC, 48000 Hz, 2ch | 308.73 MB
Genre: eLearning | Language: English​

Learn how to design production-grade GenAI systems. This course will teach you to build reliable, scalable, and cost-efficient LLM applications using advanced prompting, structured outputs, validation, and API integration.

More Info:
GenAI Model Access Layer and Structured Outputs

GenAI Inference and Serving Architecture
Last Updated Jan 30, 2026
Duration: 1h 56m | .MP4 1920×1080 30 fps(r) | AAC, 48000 Hz, 2ch | 309.29 MB
Genre: eLearning | Language: English​

Running GenAI systems efficiently is key for real-world AI. This course will teach you how to make informed model-selection decisions and implement fast, scalable, and cost-optimized transformer inference pipelines.

More Info:
GenAI Inference and Serving Architecture

GenAI for Busy Java Developers
English | 2025 | h264, yuv420p, 1280×720 | 48000 Hz, 2channels | Duration: 4h 18m | 853 MB​

Java software engineers who need to learn how to harness the capabilities of generative AI tools for critical aspects of the production software process.
This video course empowers Java engineers with the basic knowledge and skills needed to harness the capabilities of generative AI tools for various aspects of the production software process.
Developed for beginner and early intermediate Java developers, it explores the impact of Machine Learning on the Java ecosystem and features hands-on coding using tools such as OpenAI ChatGPT, Google Gemini, Anthropic Claude, and other GenAI services using the LangChain4j API. With a focus on practical applications, participants will gain proficiency in GenAI, an understanding of context, learn about embeddings, and how to responsibly integrate GenAI into Java applications.
Attendees will:
Learn the skills they need in order to apply generative AI to real-world software development. Enterprise developers will learn the fundamentals of generative AI and how to best apply them to reliably put GenAI applications into production.
Understand programmatic interfaces to GenAI using REST APIs and featuring the LangChain4J Java API, including many source code examples covering different prompt techniques, streaming, embeddings, templates, context, Retrieval-Augmented Generation (RAG) and an introduction to agents
Architect and implement a basic chatbot application that understands private document sets.
Skill Level:
Beginner to Early Intermediate
Learn How To:
Differentiate between the two basic types of deep learning
Structure prompts and select techniques that produce useful output
Use LangChain4j to create a working GenAI application
Apply embeddings to various use cases
Manage context for effective LLM responses
Choose an appropriate vector database and what to store in that database
Create tools and understand the basics of agents