Udemy - Generative AI (basic to advanced) 2025-8 Free Download

Udemy – Generative AI (basic to advanced) 2025-8
English | Tutorial | Size: 21.3 GB

Learn how Generative AI models are revolutionizing content, code, innovation using prompt engineering & real-world tech.

A warm welcome to the Generative AI (basic to advanced) course by Uplatz.

Generative AI (Generative Artificial Intelligence) refers to a type of artificial intelligence that is capable of creating new content-such as text, images, audio, code, and more-rather than simply analyzing existing data. It mimics human creativity by learning from large datasets and generating outputs that resemble original, human-made content.

What It Does

Traditional AI systems are good at recognizing patterns or making predictions based on existing data. Generative AI goes a step further by actually producing new data that didn’t exist before. For example:

Writing articles or stories

Creating images or artwork

Composing music

Writing code

Designing products or layouts

How It Works

Generative AI typically relies on advanced machine learning techniques, especially deep learning models such as:

Transformers – used in models like GPT (text) or T5

Diffusion models – used in image generation (like DALL·E or Stable Diffusion)

GANs (Generative Adversarial Networks) – used for creating realistic media

A simplified breakdown of the process:

Training

The model is trained on massive datasets (e.g., books, websites, images, code).

It learns statistical patterns, styles, and relationships in the data.

Learning Probabilities

Instead of memorizing, the model learns the probability of what should come next in a sequence (next word, next pixel, etc.).

Generation (Inference)

When you give it a prompt, it generates new content based on what it has learned.

For instance, if you type a sentence, a text model will complete it or write a full article.

If you input a concept, an image model can generate an image matching that description.

Fine-Tuning

The base model can be refined using reinforcement learning or task-specific data to make it more accurate, aligned, or safer.

Generative AI (basic to advanced) – Course Curriculum

Large Language Models (LLMs) – part 1 to 21

Coding of Transformers and LLMs – part 1 to 41

Prompt Engineering for Generative AI – part 1 to 19

AI Workflow Automation – part 1 to 7

Common Applications of Generative AI

1. Text Generation

Writing articles, blogs, and essays

Drafting emails and messages

Summarizing long documents

Translating languages

Answering questions or tutoring

2. Image Generation

Creating digital art and illustrations

Generating product mockups and logos

Designing ads, posters, and visual content

Style transfer and photo editing

3. Code Generation

Auto-completing code

Generating boilerplate scripts

Fixing bugs and refactoring code

Explaining code snippets

4. Audio and Music

Composing original music

Generating voiceovers or speech

Producing sound effects

Voice cloning and enhancement

Video Generation

Creating short films and animations

Generating explainer videos

Video summarization

Scene-to-video synthesis

5. 3D Modeling and Design

Generating 3D objects and environments

Designing virtual products or architecture

Game asset creation

6. Gaming

Procedural content and level generation

NPC (non-player character) behavior scripting

Dialogue generation

7. Fashion and Product Design

Designing apparel and accessories

Creating virtual try-ons

Generating custom product variants

8. Education

Personalized tutoring and explanations

Quiz and flashcard generation

Adaptive learning content

9. Marketing and Advertising

Writing ad copy and taglines

Creating personalized campaigns

Designing social media posts

10. Legal and Compliance

Drafting legal documents

Reviewing and summarizing policies

Identifying contract risks

11. Healthcare and Biotech

Generating radiology and diagnostic reports

Simulating molecular structures

Summarizing patient records

12. Customer Support

Chatbots for FAQs and ticket handling

Email and chat summarization

Response recommendation

13. Finance

Automating financial reports

Analyzing and summarizing earnings calls

Detecting unusual financial patterns

Benefits

Rapid content generation

Personalized or on-demand outputs

Automation of creative and technical tasks

Support for brainstorming and ideation

Time and cost efficiency for businesses

Challenges and Risks

May generate incorrect or misleading content

Can reflect biases from the training data

Risk of misuse for fake content or misinformation

Computational and environmental costs

Requires careful monitoring and human validation

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