This course is designed to become efficient AI Engineer to see yourself as one of the best IT professional. Audience: Beginners, working professionals, data scientists
Duration: 8–10 weeks (60–70 hours)
Focus: Strong fundamentals, tools, architectures, hands-on projects

What is Generative AI?

What Does a Generative AI Do?

Generative AI creates new, realistic digital content—like text, images, music, audio, and video—by learning patterns from massive datasets and then generating novel outputs in response to user prompts. It powers applications that can summarize information, answer questions, draft code, create marketing materials, and assist in product development by producing human-like, original content. 

This course will help you for,

• Future-Proof Your Career:

  Generative AI is a rapidly growing field, and acquiring skills in this area can make you a more valuable asset and a highly sought-after professional. 
• Enhance Your Current Role:

  You can apply generative AI to automate tasks, improve efficiency, and increase productivity, allowing you to focus on more strategic aspects of your job. 
• Develop Practical, Applied Skills:

  Courses provide hands-on experience with powerful tools like large language models, Hugging Face, and LangChain, enabling you to build and integrate AI into real-world scenarios. 
• Boost Creativity:

  The technology is designed to augment human creativity by helping you generate ideas, write content, design new visuals, and create innovative solutions. 

We are eager to see you on desired position by learning and implementing this skills.

Wish you all the best for your career!!!

Generative AI Masterclass – Detailed Syllabus

Module 1: Introduction to Generative AI & Modern AI
Landscape
Welcome, course objectives & outcomes
Overview of AI fields and history
Symbolic AI vs Statistical AI
Deep learning revolution
Rise of Generative AI & foundation models
Key subfields connected to GenAI
Natural Language Processing (NLP)
Computer Vision
Speech & audio processing
Discriminative vs Generative models
What each learns & produces
Real-world applications of GenAI
Text generation, chatbots, summarization
Image synthesis & editing
Code generation, video creation, design
Overview of modern GenAI ecosystem
OpenAI, Anthropic, Google DeepMind, Meta AI
Open-source communities: Hugging Face, EleutherAI, Stability AI
Ethical foundations
Bias & fairness
AI for creativity vs misuse
Importance of human-centered design

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✅ Module 2: Foundations of Deep Learning & Neural
Architectures
Deep Learning fundamentals
Neurons, weights, biases, activation functions
Feedforward networks & backpropagation
Core architectures
Perceptron & Multi-Layer Perceptron (MLP)
Convolutional Neural Networks (CNN)
Convolution layers, pooling, feature extraction
Use in images, text & time series
Recurrent Neural Networks (RNN)
LSTM, GRU for sequence modeling
Challenges: vanishing gradients, long-term dependencies
Introduction to Transformers
Self-attention mechanism
Positional encoding
Encoder, decoder, encoder-decoder structures
Embeddings & representation learning
Word2Vec, GloVe, fastText
Sentence embeddings & document embeddings
Transfer learning & fine-tuning basics
Why pre-trained models matter
Labs:
Build simple neural nets & visualize activaations
Create embeddings & explore vector spaces
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✅ Module 3: Large Language Models (LLMs) & Foundation
Models
What are LLMs?
Architecture, scale & datasets
Tokenization strategies
Byte Pair Encoding (BPE)
SentencePiece, Unigram LM
Attention & scaling tricks
Scaled dot-product, multi-head attention
Popular open-source & proprietary LLMs
GPT-2, GPT-3, GPT-4, GPT-5
LLaMa, Mixtral, Mistral
Falcon, BLOOM, PaLM2
Fine-tuning & adaptation
Full fine-tuning
Parameter-efficient tuning (LoRA, QLoRA)
Prompt tuning, prefix tuning
Evaluating LLMs
Automatic metrics: BLEU, ROUGE, METEOR
Human evaluation
Strengths & limitations
Hallucinations, factuality, prompt sensitivity
Practical labs:
Query models on Hugging Face
Visualize attention patterns
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✅ Module 4: Core Generative AI Architectures &
Techniques
Generative Adversarial Networks (GANs)
Generator vs discriminator concept
Conditional GANs, CycleGAN, StyleGAN
Applications: style transfer, deepfakes, data augmentation
Variational Autoencoders (VAEs)
Encoder, latent space, decoder
Sampling & reconstruction
Applications: anomaly detection, image synthesis
Diffusion models
Forward & reverse diffusion
Popular models: Stable Diffusion, DALL·E, ControlNetUse in image, video & audio generation
Transformer-based generative models
Sequence-to-sequence models
Encoder-decoder vs decoder-only architectures
Foundation models & generalization
Cross-modal learning
Multimodal embeddings
Advanced topics (overview)
Reinforcement Learning from Human Feedback (RLHF)
Direct Preference Optimization (DPO)
Labs:
Generate images with pre-trained diffusion models
Text-to-text generation using open-source transformers
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✅ Module 5: Practical NLP & Hugging Face Ecosystem
NLP pipeline essentials
Tokenization, stopword removal, lemmatization, stemming
Embeddings & semantic search
Dense vs sparse retrieval
Vector stores: FAISS, Pinecone, Chroma
Hugging Face Transformers
Model Hub, Datasets, Tokenizers libraries
Loading & using pre-trained models
Fine-tuning text classifiers
Dataset preparation
Trainer API & evaluation
Hugging Face Accelerate & PEFT (Parameter Efficiennt Fine-Tuning)
Using model cards & licensing responsibly
Mini-projects:
Text summarizer
Sentiment classifier
====================================================
✅ Module 6: Prompt Engineering & Retrieval Augmented
Generation (RAG)
Basics of prompting
Zero-shot, one-shot, few-shot
Advanced prompting
Chain-of-thought, self-consistency
Tree of thought prompting
Designing prompts systematically
Templates, variables, evaluation
RAG pipeline architecture
Chunking strategies & granularity
Embedding generation & vector search
Re-ranking retrieved passages
Popular RAG frameworks
LangChain basics: chains, agents, memory
LLaMaIndex integration
Best practices
Avoiding prompt injection & leakage
Combining retrieval & generation
Hands-on:
Build a QA system with RAG
Experiment with prompt design
====================================================
✅ Module 7: Tools & Libraries Beyond Hugging Face
LangChain
Prompt templates, chains, conversational memory
Agents & function calling
OpenAI & Anthropic APIs
Cohere & AI21 APIs overview
LLaMaIndex for indexing & retrieval
spaCy & NLTK for text processing
Visual tools & experimentation
Hugging Face Spaces
Streamlit / Gradio demos
Tips on benchmarking models
====================================================
✅ Module 8: Multimodal & Creatiive AI
Introduction to multimodal AI
Combining text, images, audio
CLIP: vision + language
Flamingo & BLIP models
Audio models (overview)
Whisper, Bark, audio diffusion
Generative AI for creative fields
Design, music, video & animation
Responsible creative AI
Copyright & fair use
Project idea exploration:
Image captioning
Text-to-image apps
AI storytelling
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✅ Module 9: Real-World Use Cases & Guided Projects
Understanding product & business use cases
Customer service, summarization tools, creative writing, marketingDesigning projects
Scoping & defining goals
Data sourcing & cleaning
Choosing models & evaluating results
Hands-on guided project
Summarizer for PDFs
Chatbot for FAQ
Documenting & sharing work
Notebooks, GitHub, Hugging Face Spaces
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✅ Module 10: Ethics, Impact & Future Trends
Bias & fairness in LLMs
Hallucinations & transparency
AI and the job market
New roles: prompt engineer, LLM ops, AI analyst
AI & creativity: augmentation, not replacement
Emerging trends
Long-context models, memory & self-reflection
Agentic workflows
Multimodal & cross-domain AI
Responsible innovation & human-centered AI

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You will get certification after successful course completion.

Students who are looking for job after completion of the course

1. Should be Graduate(From IT /Computer Science) ,ME, BE, M Tech. with Min 50% Passing Criteria
2. Qualifications Include: BE/ME/MTech(ALL BRANCHES), BCA/MCA,BCS/MCS,BSC(IT/CS) ,PG(IT/CS)
3. No coding background necessary.
4. Knowledge of basic coding is preferred.
5. Anyone who wants boost knowledge and switch the job.
6. Anyone who is having Laptop/ Computer.
7. Anyone who wants to switch jobs to Data Science.
8. Basic knowledge of English is mandatory.

1. Live Interactive Sessions will take you to the industry based learning and activities fulfill the need of Industry.
2. Hands-on project knowledge
3. Notes provided by expert trainer
4. There are career opportunities available like AI Engineer and Gen AI.
5. A beginner also go for this training and explore may opportunities

Kindly scroll down to see FAQs.