Generative AI Program
Unlock the full potential of Generative AI with a curriculum built for real-world impact. This comprehensive course guides learners from foundational concepts in NLP and language models to advanced implementations like GANs, VAEs, and Retrieval-Augmented Generation. With a strong focus on practical coding, project-based learning, and deployment-ready skills, you'll graduate with the ability to build and scale powerful AI applications using state-of-the-art tools and techniques.
Who Should Enroll?
This course is ideal for:
- Software developers looking to integrate AI features into applications.
- Data scientists and ML engineers wanting hands-on generative AI experience.
- Tech professionals and researchers seeking applied knowledge in NLP, transformers, and image generation.
- Beginner to intermediate learners ready to level up with real coding projects and advanced AI architectures.
No prior AI experience required—just a strong interest in building intelligent systems
What You'll Learn
Structured across eight progressive modules and a capstone project, the curriculum includes:
1.Fundamentals of Generative AI & NLP
- Tokenization, Named Entity Recognition, Word Embeddings, GPT-3, BERT, VAE, GANs.
2.Language Models & LangChain Integration
- Workflow automation and multi-step QA systems with LangChain.
3.Fine-Tuning Transformers with Hugging Face
- Customize BERT, GPT-2, and T5 for text generation and question answering.
4.Building with Retrieval-Augmented Generation (RAG)
- Enhance language models using RAG for scalable, contextual QA systems.
5.Image Generation with GANs & VAEs
- Implement DCGAN, CycleGAN, StyleGAN, and VAEs for image synthesis and translation.
6.Advanced NLP and GAN Architectures
- Train WGANs, CGANs, and fine-tune BERT for domain-specific use cases.
7.Deployment and Scalability
- Launch real-time AI apps on AWS, Azure, or GCP using Docker and Streamlit.
8.Real-World Use Cases & Ethical AI
- Apply Generative AI in healthcare, finance, and entertainment while addressing fairness and bias.
Learning Outcomes
By the end of this course, you will be able to:
- Build and fine-tune cutting-edge language and vision models.
- Design multi-step NLP workflows with LangChain and Hugging Face.
- Implement and train GANs and VAEs for image generation.
- Deploy production-ready AI apps using modern cloud infrastructure.
- Tackle ethical considerations in real-world AI systems.
- Deliver a capstone project that showcases end-to-end AI capabilities.
Real-World Projects Include
- Text classification model with NLP fundamentals
- Multi-step question answering system using LangChain
- Fine-tuned text generator or QA model on Hugging Face
- RAG-powered internal knowledge assistant
- CycleGAN-based image translator or VAE-powered generator
- GAN-based image application or BERT-powered QA engine
- Deployed GPT chatbot or real-time document summarizer
- Capstone: End-to-end AI application with deployment
Course Curriculum
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Key Generative Models: GPT-3, VAE, GANs, BERT, RAG.
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Basic NLP tasks: Tokenization, NER, and Word Embeddings.
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Build a text classification model.
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Transformer architecture, GPT-3, BERT, T5.
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LangChain for workflow integration.
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Build a multi-step QA system with LangChain.
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Use of Hugging Face Hub, fine-tuning BERT, GPT-2, T5, and more.
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Fine-tune a model for text generation or QA.
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Introduction to RAG and its integration with generative models.
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Build a RAG-based QA system.
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Autoencoders and VAEs for image generation.
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GANs: DCGAN, CycleGAN, StyleGAN for image synthesis.
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Implement a VAE for image generation or CycleGAN for translation.
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Advanced BERT fine-tuning for NER, classification, QA.
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WGAN and CGAN for stable image generation.
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Build a BERT-powered QA system or GAN-based image app.
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Deploy models on AWS, Google Cloud, or Azure.
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Build user-friendly interfaces with Streamlit.
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Deploy a GPT chatbot or text summarizer.
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Fine-tuning for domain-specific applications.
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Ethical AI, fairness, and mitigating bias.
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Case studies in entertainment, healthcare, finance.
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Capstone Project: Design and deploy a Generative AI application, such as:
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Text-generation chatbot using LLMs (e.g., GPT-3) and LangChain.
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RAG-based internal FAQ system.
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Generative art with CycleGAN or StyleGAN.
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Real-time document summarizer.
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Tools & Technologies:
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LangChain, Hugging Face (Transformers, Datasets, Model Hub)
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Streamlit (for web apps)
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RAG, TensorFlow, PyTorch, Docker
Jonathan Campbell
- 72 Videos
- 102 Lectures
- Exp. 4 Year
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PLACEMENT ASSISTANCE
*MIN 5 COMPANY WALK-INS
GitHub portfolio and Job-ready resume to enhance their career prospects.
- Resume & Linkedin Building
- 1-1 Mock Interviews
- 100% Hands-on
- Certification
Tools Covered
Throughout the course, you'll gain hands-on experience with:
PyTorch
TensorFlow
Transformers
LangChain
BERT
GPT-2
T5
VAE
DCGAN
CycleGAN
StyleGAN
AWS
Google Cloud
Azure
Streamlit
Docker
RAG
Fine-Tuning
Transfer Learning
Workflow Orchestration
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