Generative AI Course
Perks Of Learning Generative AI Course
Plug Into the GenAI Vanguard
Access an elite network of engineers, researchers, and innovators shaping the future of Generative AI across LLMs, VLMs, and Agents.
Industry-Trusted Certification
Earn a GenAI Specialization Certificate that signals your readiness to build with cutting-edge models like LLaMA, Qwen, and Mamba.
Flexible, Deep Learning Journey
Master GenAI at your own pace with a modular curriculum packed with projects, mentorship, and toolchain walkthroughs.
Master Tools That Build the Future
Get hands-on with LangChain, LlamaIndex, Vector DBs, FastAPI, and LangGraph to architect AI-powered systems and agents.
Real-Time Research Access
Stay months ahead with curated updates on LLM architectures, fine-tuning breakthroughs, and the latest in multimodal AI.
AI Career Launchpad
Unlock portfolio reviews, 1:1 resume rewrites, mock interviews, and job connections tailored to AI engineering roles.
Mentorship from Core AI Engineers
Learn directly from engineers building real-world GenAI systems at top startups, labs, and AI-driven product teams.
Deploy Real GenAI Products
Build and ship production-grade GenAI apps—chatbots, image models, RAG workflows—with a GitHub-ready portfolio.
Train with Proven AI Stacks
Build confidence with real-world frameworks: PyTorch, Hugging Face, Redis, CrewAI, AutoGen, and vectorized retrieval.
Who Should Join This Generative AI Course?
This course is built for professionals aiming to go beyond API usage and develop deep, architecture-level skills in building, fine-tuning, and deploying advanced Generative AI models—including LLMs, VLMs, and multimodal systems.
Course learning objectives
Top Skills You’ll Gain In Generative AI Course
Generative AI Tools & Frameworks You’ll Master
PyTorch is one of the most popular frameworks for developing deep learning models. Its dynamic nature makes it popular for research and production, helping users build neural networks for various applications like image recognition and NLP.
AI Architectures & Fine-Tuning
Master Advanced AI Models & Techniques
In this course, you'll explore the latest AI architectures and their fine-tuning techniques, gaining hands-on experience with state-of-the-art models used in industries such as healthcare, finance, and language generation.
LLaMA3
What it is
LLaMA3 is Meta’s newest large language model designed for long-form text generation, translation, and NLP tasks. It now supports over 30 languages and has been trained on 15 trillion tokens, offering improved efficiency, accuracy, and faster performance compared to earlier versions.
Fine-Tuning
With LoRA (Low-Rank Adaptation) and RAG (Retrieval-Augmented Generation), LLaMA3 can be fine-tuned to deliver highly efficient, relevant, and cost-effective outputs, while using fewer computational resources.
Applications
Used in NLP tasks like chatbots, language translation, and text summarization. Custom conversational AI for enterprises. Large-scale text generation and document summarization.
Mistral
What it is
Mistral is a lightweight and powerful model optimized for domain-specific tasks in industries like finance and healthcare. It offers exceptional precision while being adaptable to smaller, niche applications with minimal resource usage.
Fine-Tuning
Mistral uses LoRA to minimize computational costs while enhancing domain-specific outputs, making it ideal for high-quality results in specialized tasks.
Applications
Used in medical diagnoses and clinical data analysis. Financial risk assessments, fraud detection, and algorithmic predictions. Niche tasks requiring targeted AI solutions for smaller datasets.
Gemini
What it is
Gemini is Google DeepMind’s most advanced multimodal model, combining vision, language, and reasoning into one unified system. It excels in complex tasks like image understanding, text generation, and multimodal reasoning with high accuracy.
Fine-Tuning
Gemini supports advanced fine-tuning methods, enabling optimized performance for multimodal tasks involving text, vision, and audio. This ensures high adaptability across various industries.
Applications
Used in creative content generation integrating images and text. Development of autonomous agents and decision-making systems. Visual understanding tasks like object detection and multimodal reasoning.
Techniques Explained for Generative AI
LoRA (Low-Rank Adaptation)
What it is
LoRA fine-tunes large models by focusing on specific layers, reducing the number of trainable parameters. This enables faster and more efficient customization of massive models like LLaMA3 and Gemini.
How it works
LoRA adapts the model weights by introducing low-rank matrices, ensuring fine-tuning is lightweight while maintaining high performance.
Why it matters
LoRA is ideal for low-resource environments, enabling businesses to fine-tune models cost-effectively without retraining the full model.
RAG (Retrieval-Augmented Generation)
What it is
RAG improves model outputs by retrieving real-time information from external sources. It combines a retrieval system with a pre-trained language model to produce accurate, context-aware responses.
How it works
RAG searches for relevant documents during inference and integrates the retrieved content into the model’s generation pipeline. This ensures answers are grounded in external knowledge.
Why it matters
RAG is perfect for tasks requiring up-to-date factual information, such as: Customer support systems. Research tools. Dynamic question-answering systems.
Multimodal Techniques
What it is
Multimodal AI integrates text, images, and other data types into a single model. Models like Gemini and LLaMA3 Vision combine modalities to deliver advanced reasoning and generation capabilities.
How it works
Multimodal systems process multiple inputs (e.g., images and text), align them into a shared representation, and generate outputs that combine insights from all modalities.
Why it matters
Multimodal techniques power: Visual Question Answering (VQA) for understanding images and text. Content generation blending visuals and natural language. Autonomous systems that require diverse input analysis for decision-making.
Generative AI Mastery Roadmap
Foundation Refresher
Reboot your AI mindset with core foundations: • Python for AI: Data structures, OOP, NumPy, Pandas • Math for ML: Vectors, matrices, dot product, eigenvalues • Probability & Stats: Bayes, Gaussian, mean/variance • ML Basics: Linear & Logistic Regression, SVM, Decision Trees • Toolkits: Scikit-learn, Matplotlib, Seaborn
Neural Network Essentials
Understand the DNA of deep learning systems: • Perceptron, Activation & Loss Functions • Gradient Descent: Vanilla, SGD, Adam, RMSProp • Neural Networks & Backpropagation • CNNs: Conv, Pooling, ResNet, EfficientNet • RNNs, GRU, LSTM – Sequence Modeling • GNNs Intro: Graph convolutions & node classification • Framework: PyTorch + TensorBoard
Applied Deep Learning
Use DL to solve real-world problems: • Vision: Image classification, object detection (YOLOv8), segmentation (U-Net) • NLP: Text classification, NER, summarization • Audio: Speech-to-text, audio tagging • Projects: Face mask detector, sentiment classifier, voice command recognizer • Deploy using ONNX, TorchScript
Generative AI Fundamentals
Build creative AI with generative architectures: • Latent Representations & Sampling • Autoencoders: Regular & Variational • GANs: Vanilla, StyleGAN2, CycleGAN • Diffusion Models: DDPM, Latent Diffusion, ControlNet • Prompt-to-Image & Prompt2Prompt • Tools: Hugging Face Diffusers, ComfyUI, AUTOMATIC1111
Finetuning GenAI Models
Customize powerful models: • Techniques: SFT, LoRA, QLoRA, DAPT, PEFT • RLHF Stack: PPO → DPO → RLAIF • Prompt vs. Parameter Tuning • Evaluation: BLEU, ROUGE, Perplexity • Libraries: Axolotl, PEFT, TRL • Real-World: Finetune LLaMA, Mistral, Phi
Multimodal AI Architectures
Create systems that see, speak, understand: • Fusion Types: Early, Cross-Attention, Late • Modalities: Text, Image, Audio, Video • Models: Kosmos-2, GPT-4V, MM-ReAct • Use Cases: Interactive tutors, enterprise bots, medical diagnostics • Tools: HuggingFace, LangChain Multi-Modal, LLaVA+LangGraph
GenAI for Vision
Unlock vision-language synergy: • VLMs: CLIP, BLIP-2, Flamingo, LLaVA, Gemini • Text-to-Image: Stable Diffusion XL, DALL·E 3, MidJourney • Segmentation + Diffusion: ControlNet, SAM, DragGAN • ViT, DETR architectures • Applications: Image captioning, VQA, visual search • Tools: OpenCLIP, Diffusers, Gradio, Streamlit
LLMs Demystified
Decode large language models: • Transformer Architecture: Attention, Multi-head, Feedforward • Positional Encoding: Sinusoidal, ROPE, ALiBi • GPT, BERT, T5, BART — comparative breakdown • Tokenization: BPE, SentencePiece • Sampling: Greedy, Beam, Top-k, Top-p • Model internals: KV Cache, LayerNorm, Residual Paths • Libraries: HuggingFace Transformers, OpenAI API, vLLM
Retrieval-Augmented Generation (RAG)
Supercharge LLMs with external knowledge: • Chunking: RecursiveTextSplitter, Semantic Chunking • Embeddings: OpenAI, HuggingFace, Cohere, Instructor • Vector DBs: FAISS, Qdrant, Pinecone, Weaviate • Tools: LangChain, LlamaIndex, Haystack • Advanced: Hybrid Retrieval, Graph-RAG, Rerankers • Use Cases: Legal/Medical assistants, custom chatbots
Model Quantization & Serving
Speed & scale GenAI: • Quantization: INT8, GPTQ, AWQ, SmoothQuant • Compression & Distillation: DistilBERT, TinyLLaMA • Fast Inference: vLLM, DeepSpeed-MII, FasterTransformer • Serving: Triton, FastAPI, BentoML • Hosting: HF Hub, Replicate, Modal Labs
Reasoning & RLHF
Infuse logic & alignment: • Chain-of-Thought, ReAct, Toolformer • Function Calling: LangChain, OpenAI tools • Memory & Context Injection • RLHF: PPO, DPO, RLAIF • Eval: TruthfulQA, MT-Bench, AlpacaEval
Agentic AI Introduction
Build autonomous AI agents: • Agent Types: Reflex, Goal-based, Utility, Learning • SDKs: AutoGen, CrewAI, LangGraph, Semantic Kernel, SuperAgent • Core Loops: Sense → Think → Act • Multi-Agent: DAG, Role-based, chat loops • Use Cases: Sales assistants, debugging agents, meeting orchestrators
Foundation Refresher
Reboot your AI mindset with core foundations: • Python for AI: Data structures, OOP, NumPy, Pandas • Math for ML: Vectors, matrices, dot product, eigenvalues • Probability & Stats: Bayes, Gaussian, mean/variance • ML Basics: Linear & Logistic Regression, SVM, Decision Trees • Toolkits: Scikit-learn, Matplotlib, Seaborn
Neural Network Essentials
Understand the DNA of deep learning systems: • Perceptron, Activation & Loss Functions • Gradient Descent: Vanilla, SGD, Adam, RMSProp • Neural Networks & Backpropagation • CNNs: Conv, Pooling, ResNet, EfficientNet • RNNs, GRU, LSTM – Sequence Modeling • GNNs Intro: Graph convolutions & node classification • Framework: PyTorch + TensorBoard
Applied Deep Learning
Use DL to solve real-world problems: • Vision: Image classification, object detection (YOLOv8), segmentation (U-Net) • NLP: Text classification, NER, summarization • Audio: Speech-to-text, audio tagging • Projects: Face mask detector, sentiment classifier, voice command recognizer • Deploy using ONNX, TorchScript
Generative AI Fundamentals
Build creative AI with generative architectures: • Latent Representations & Sampling • Autoencoders: Regular & Variational • GANs: Vanilla, StyleGAN2, CycleGAN • Diffusion Models: DDPM, Latent Diffusion, ControlNet • Prompt-to-Image & Prompt2Prompt • Tools: Hugging Face Diffusers, ComfyUI, AUTOMATIC1111
LLMs Demystified
Decode large language models: • Transformer Architecture: Attention, Multi-head, Feedforward • Positional Encoding: Sinusoidal, ROPE, ALiBi • GPT, BERT, T5, BART — comparative breakdown • Tokenization: BPE, SentencePiece • Sampling: Greedy, Beam, Top-k, Top-p • Model internals: KV Cache, LayerNorm, Residual Paths • Libraries: HuggingFace Transformers, OpenAI API, vLLM
GenAI for Vision
Unlock vision-language synergy: • VLMs: CLIP, BLIP-2, Flamingo, LLaVA, Gemini • Text-to-Image: Stable Diffusion XL, DALL·E 3, MidJourney • Segmentation + Diffusion: ControlNet, SAM, DragGAN • ViT, DETR architectures • Applications: Image captioning, VQA, visual search • Tools: OpenCLIP, Diffusers, Gradio, Streamlit
Multimodal AI Architectures
Create systems that see, speak, understand: • Fusion Types: Early, Cross-Attention, Late • Modalities: Text, Image, Audio, Video • Models: Kosmos-2, GPT-4V, MM-ReAct • Use Cases: Interactive tutors, enterprise bots, medical diagnostics • Tools: HuggingFace, LangChain Multi-Modal, LLaVA+LangGraph
Finetuning GenAI Models
Customize powerful models: • Techniques: SFT, LoRA, QLoRA, DAPT, PEFT • RLHF Stack: PPO → DPO → RLAIF • Prompt vs. Parameter Tuning • Evaluation: BLEU, ROUGE, Perplexity • Libraries: Axolotl, PEFT, TRL • Real-World: Finetune LLaMA, Mistral, Phi
Retrieval-Augmented Generation (RAG)
Supercharge LLMs with external knowledge: • Chunking: RecursiveTextSplitter, Semantic Chunking • Embeddings: OpenAI, HuggingFace, Cohere, Instructor • Vector DBs: FAISS, Qdrant, Pinecone, Weaviate • Tools: LangChain, LlamaIndex, Haystack • Advanced: Hybrid Retrieval, Graph-RAG, Rerankers • Use Cases: Legal/Medical assistants, custom chatbots
Model Quantization & Serving
Speed & scale GenAI: • Quantization: INT8, GPTQ, AWQ, SmoothQuant • Compression & Distillation: DistilBERT, TinyLLaMA • Fast Inference: vLLM, DeepSpeed-MII, FasterTransformer • Serving: Triton, FastAPI, BentoML • Hosting: HF Hub, Replicate, Modal Labs
Reasoning & RLHF
Infuse logic & alignment: • Chain-of-Thought, ReAct, Toolformer • Function Calling: LangChain, OpenAI tools • Memory & Context Injection • RLHF: PPO, DPO, RLAIF • Eval: TruthfulQA, MT-Bench, AlpacaEval
Agentic AI Introduction
Build autonomous AI agents: • Agent Types: Reflex, Goal-based, Utility, Learning • SDKs: AutoGen, CrewAI, LangGraph, Semantic Kernel, SuperAgent • Core Loops: Sense → Think → Act • Multi-Agent: DAG, Role-based, chat loops • Use Cases: Sales assistants, debugging agents, meeting orchestrators
Industry-Trusted Generative AI Certificate
Industry-Trusted Generative AI Certificate
Upon completing this Generative AI course, you’ll receive a globally recognized certification— proof that you’ve mastered fine-tuning, prompt engineering, and real-world deployment of LLMs and GenAI tools. Whether you’re upskilling or switching careers, this certificate validates your hands-on expertise in building and deploying advanced AI models.
What Sets Us Apart?
| Feature | Our Program | Other Courses |
|---|---|---|
| LLMs, RAG & Agentic AI Modules | ✔ Learn LangChain, LangGraph, AutoGen, and RAG systems in depth with real implementations | ✘ Most courses skip RAG or agentic workflows and only teach prompt-based APIs |
| Project-Based Curriculum | ✔ 20+ projects from LLM fine-tuning to building full-stack GenAI apps for real-world domains | ✘ Typically offer 1–2 guided assignments with limited scope and no deployment |
| Deployment & Scaling | ✔ Covers FastAPI, Docker, Kubernetes, and vector databases for scalable AI systems | ✘ Deployment not covered — limited to notebook-based demos |
| Tooling Ecosystem Mastery | ✔ Hands-on with Hugging Face, Diffusers, Pinecone, ChromaDB, LangChain, and OpenAI SDKs | ✘ Tool coverage limited to surface-level API tutorials |
| Live Mentorship & Expert Sessions | ✔ Weekly live sessions, 1-on-1 mentorship, and real-time doubt clearing with GenAI experts | ✘ Generic pre-recorded content, no dedicated technical mentorship |
| Capstone Use Cases | ✔ Solve real business problems with GenAI — like AI tutors, internal copilots, and RAG search engines | ✘ Mostly academic or generic chatbot projects with no real-world context |
| Mock Interviews & Resume Help | ✔ 10+ mock interviews + personalized resume reviews tailored for GenAI engineering roles | ✘ No job-readiness focus — mostly theoretical or platform-based |
| Placement Support & Hiring Network | ✔ Access to exclusive hiring partners, job referrals, and industry-aligned certification | ✘ Often lacks real placement help beyond a completion certificate |
Generative AI Course Fees
Additional Benefits:
- Job Assistance: Our program ensures support until you secure a role. 100% Placement Program: We are fully committed to helping you find the right opportunity.
- Real-World Projects: Gain hands-on experience with projects based on real industry scenarios.
- Comprehensive Curriculum: Gain expertise in generative AI across text, image, and audio applications, including multimodal AI.
What Our Learners Say
Hear real experiences from professionals who’ve completed this course
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