Core concepts of Generative AI

Core concepts of Generative AI
Last updated 12/2025
Duration: 9h 11m | .MP4 1280×720 30fps(r) | AAC, 44100Hz, 2ch | 2.79 GB

Text preprocessing, glove, LSTM, Introduction to attention, Introduction to self-attention, Multi-Head Self Attention

What you’ll learn
– Text preprocessing
– tokenization
– lemmatization
– bag of words
– TF-IDF
– n-gram
– Word2Vec
– continuous bag of words
– skip gram
– glove
– Neuron
– word embeddings
– Convolutional Neural Network
– Recurrent neural network
– LSTM
– Introduction to GAN
– Introduction to attention
– Introduction to Transformer architecture
– Introduction to self-attention
– Introduction to Multi-Head Self Attention
– Introduction to Positional encoding
– Introduction to Encoder-decoder structure
– Introduction to BERT
– Introduction to GPT
– What is LLM
– Introduction to BLEU
– Introduction to FID
– Introduction to extrinsic evaluation metrics?
– Introduction to fine tuning
– Introduction to multimodal foundation model
– Introduction foundation models
– Introduction to full fine-tuning
– Introduction to parameter-efficient fine-tuning (PEFT)
– Introduction to adapter based fine tuning
– Introduction to emergent abilities
– Introduction to unsupervised learning
– Introduction to masked language modeling (MLM)
– Introduction to self supervised learning
– Introduction to contrastive learning
– Introduction to reinforcement learning from human feedback (RLHF)?
– Introduction to knowledge distillation

Requirements
– Basic and advanced knowledge is required
– No need to know about generative AI

Description
Learners will explore the evolution of generative AI, from early probabilistic models to today’s large language models (LLMs) such as GPT, Claude, Llama, and diffusion-based image generators like Stable Diffusion and Midjourney. Through hands-on exercises, students will practice prompt engineering, fine-tuning, evaluation methods, and responsible AI principles.

By the end of the course, students will understandhow generative AI works,how to use it effectively, andhow to apply it to real-world tasksacross industries such as education, marketing, software development, and creative content production.

Learning Outcomes

Upon completing this course, learners will be able to:

Explain the fundamental concepts behind generative AI and machine learning.

Understand the architecture and training principles of large language models and diffusion models.

Understand generative AI tools.

Evaluate generative AI outputs for accuracy, bias, and safety.

Understand model fine-tuning, and embeddings.

Apply generative AI to solve practical problems through mini-projects.

Topics Covered

Large Language Models (GPT, Llama, Claude, Gemini)

Transformers & Attention Mechanisms

Diffusion Models for Image Generation

Fine-tuning and Masked Language Models Concepts

Introduction to BLEU

Introduction to FID

Retrieval-Augmented Generation (RAG)

Real-world Applications Across Industries

Who Should Take This Course?

This course is ideal for:

Students new to AI

Software developers and IT professionals

Digital content creators

Business professionals exploring AI integration

Anyone interested in understanding or applying generative AI

No advanced mathematics experience is required-just a willingness to explore and experiment.

Who this course is for:
– Anyone who wants to improve python skills
– Anyone who wants to get into generative AI fields
– Anyone who wants to improve AI skills
– Anyone who wants to become expert in generative AI fields
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