Overview

  • Objective:
    The objective of this program is to transform motivated beginners into confident AI practitioners who can understand, design, and implement practical AI solutions. By the end of the course, participants will be able to:

    1. Grasp the fundamental principles of modern AI and large language models.

    2. Build real-world AI applications that integrate text, image, and audio processing.

    3. Apply vector search, retrieval, and multimodal techniques to enhance AI performance.

    4. Deploy AI systems efficiently on modern hardware and in production environments.

    5. Develop the problem-solving mindset and hands-on experience required to innovate with AI technologies.

By

Venu thota

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Generative AI

Category:

0
24

Enrollments

Level

Beginner

Time to Complete:

5 hours 0 minute

Lessons:

10

Certificate:

Yes

One-time for 1 person

Free

Overview

  • Objective:
    The objective of this program is to transform motivated beginners into confident AI practitioners who can understand, design, and implement practical AI solutions. By the end of the course, participants will be able to:

    1. Grasp the fundamental principles of modern AI and large language models.

    2. Build real-world AI applications that integrate text, image, and audio processing.

    3. Apply vector search, retrieval, and multimodal techniques to enhance AI performance.

    4. Deploy AI systems efficiently on modern hardware and in production environments.

    5. Develop the problem-solving mindset and hands-on experience required to innovate with AI technologies.

What You’ll Learn?

Learn how modern AI systems actually work under the hood — not just how to use them.
Understand how neural networks make predictions, learn from examples, and recognize new patterns they’ve never seen before.
Discover why today’s most popular AI models use specific designs — and what makes each design useful.
Break down the idea of attention and why Transformer models (like ChatGPT) became the standard for generative AI.

Requirements

Before starting this course, you should have:
Basic Python skills (loops, functions, data structures).
Familiarity with NumPy or another numerical computing library.

Syllabus Overview

10

Lessons

2

Quizzes

2

Tasks

1

Resources

MODULE 1 Generative AI Foundations

MODULE 2 Deep Learning Essentials for Generative AI

MODULE 3 Transformer Architecture & LLM Internals

MODULE 4 Embeddings, Vector Databases & Semantic Search

MODULE 5 Retrieval-Augmented Generation (RAG) Systems

Material Includes

Well-designed slide decks that explain every topic clearly and visually.
Complete coding notebooks in both PyTorch and TensorFlow so you can practice as you learn.
Easy-to-follow diagrams showing how neural networks, attention, and Transformers actually work.
Practical exercises based on real deep-learning problems you would face in real projects.

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