Core Concepts Of Generative Ai
 

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
Variational Autoencoders (VAEs)
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
Core Concepts of Generative AI is an introductory-to-intermediate course designed to equip learners with a strong foundational understanding of generative artificial intelligence-its theories, methods, tools, and real-world applications. This course demystifies how modern AI systems create text, images, audio, and other content, while helping students develop the technical intuition needed to work confidently with generative models.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 understand how generative AI works, how to use it effectively, and how to apply it to real-world tasks across industries such as education, marketing, software development, and creative content production.Learning OutcomesUpon 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 CoveredIntroduction to Artificial Intelligence & Machine LearningLarge Language Models (GPT, Llama, Claude, Gemini)Transformers & Attention MechanismsDiffusion Models for Image GenerationFine-tuning and Masked Language Models ConceptsIntroduction to BLEUIntroduction to FIDRetrieval-Augmented Generation (RAG)Real-world Applications Across IndustriesWho Should Take This Course?This course is ideal for:Students new to AISoftware developers and IT professionalsDigital content creatorsBusiness professionals exploring AI integrationAnyone interested in understanding or applying generative AINo 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