Master Generative AI, LLM-based AI Agents & Agentic systems
Learn from instructor-led sessions with real-world applications
Features a vast curriculum with 21 modules & capstone projects
Receive the advanced certificate in Applied Gen AI & Agentic Systems
Work with tools like ChatGPT, LangChain & AutoGPT
Ideal for IT professionals, students & entrepreneurs
Includes practical labs to build multimodal AI applications
Enjoy flexible learning that fits your personal schedule
Affordable payment choices available through instalments
What will you learn from us:
Upcoming sessions
Why deep learning?
ANN structure, activation functions
Feedforward and backpropagation
Build ANN using Keras
TensorFlow 2.x and Keras API
Loss functions and optimizers
Overfitting and underfitting
Train a churn model on tabular data
NLP use cases: chatbots, sentiment, classification
Preprocessing: tokenization, stop words, lemmatization
Vectorization: Bag of Words, TF-IDF
Hands-on: Sentiment analysis
Logistic regression and Naive Bayes
Word embeddings introduction
Build: spam or feedback classifier
CNN layers: filters, pooling, convolution
Applications: image classification, document scan
Hands-on: Use pre-built CNN (transfer learning)
Save/load models (Pickle, Joblib, TF)
Create Flask/FastAPI REST APIs
Deploy a model (e.g., churn or sentiment)
Agent, environment, state, action, reward
Exploration vs exploitation
Q-learning (tabular)
Hands-on: Grid-world agent simulation
Self-attention and multi-head attention
BERT vs GPT
Positional encoding
Hands-on: Text generation with transformer
Generator vs Discriminator
DCGAN, CycleGAN, StyleGAN
Hands-on: Synthetic image generation
Denoising Diffusion Probabilistic Models (DDPM)
Diffusion vs GANs
Tools: Stable Diffusion, DALLĀ·E
Hands-on: Generate text-to-image outputs
GPT, Claude, Falcon, Mistral overview
Prompt engineering
Fine-tuning domain-specific LLMs
Hands-on: Legal/Medical chatbot
LangChain architecture
Vector databases (FAISS, Pinecone)
Build RAG pipeline
Hands-on: Doc-aware Q&A chatbot
Vision-Language Models: GPT-4V, CLIP, BLIP
Text-to-image/video/audio generation
Hands-on: Generate media from prompts
Content generation pipelines
Brand asset generation
Tools: Sora, Runway, DALLĀ·E
Deep Q-Networks (DQN)
Policy Gradients
PPO (Proximal Policy Optimization)
Hands-on: Train an autonomous agent
Multi-agent collaboration
LangChain dynamic decision agents
Build a simple autonomous assistant
Task decomposition & planning
Reflex vs learning agents
CrewAI orchestration
Hands-on: Multi-agent research + writing task
LangChain tools: Google Search, Wolfram, Zapier
Agent memory: vector, long-term, summary
Context retention strategies
Hands-on: Web-aware document chatbot
OpenAI, Claude function calling
ReAct prompting strategy
Toolformer concept
Hands-on: Reasoning assistant with tools & APIs
Orchestrating prompt ā tools ā output
Guardrails, observability
Evaluation metrics: latency, task completion
Hands-on: AI assistant to summarize meetings, update CRM, draft reports
Ethics, safety, bias, hallucinations
Gradio, Streamlit, Hugging Face Spaces
Hands-on: Host your GenAI app publicly
LLM-powered task assistant with RAG
LangGraph + CrewAI agent orchestration
Integration with external tools/APIs (e.g., calendar, CRM, Google Search)
ReAct-based decision flow
Deployed on Streamlit or Hugging Face
Performance evaluation (task completion, responsiveness, hallucination %)
Meeting summarizer + action item generator
Business intelligence research assistant
Autonomous marketing campaign writer
Project planner with goal decomposition
After you complete your training, you will be able to:
1
Create AI agents with LangChain, AutoGPT and CrewAI tailored for practical real-world applications
2
Master NLP techniques using ChatGPT, LangChain and transformer-based frameworks for advanced language processing tasks
3
Apply reinforcement learning through Q-learning, PPO methods and Grid-world simulations to solve dynamic problems
4
Build multimodal AI solutions integrating GPT-4V, CLIP and text-to-image models for innovative outputs
5
Generate synthetic images and datasets using GANs such as DCGAN and CycleGAN for diverse applications
6
Deploy autonomous AI solutions utilising CrewAI and LangGraph to deliver real-time intelligent performance
Before enrolling in the Advanced Certification in Applied Gen AI and Agentic Systems, the eligibility requirements are as follows:
Overall ratings by our students
The Advanced Certification in Applied Gen AI and Agentic Systems is a specialised program designed to provide professionals with hands-on expertise in cutting-edge AI technologies. It covers advanced topics such as deep learning, natural language processing, generative AI and reinforcement learning. This prepares you to design intelligent and adaptive systems.
Unlike generic AI courses, our program emphasises applied learning with real-world projects using tools like LangChain, AutoGPT and transformers. We enable you to build autonomous agents and multimodal AI systems that drive innovation across industries.
The Advanced Certificate in Applied Gen AI and Agentic Systems will guide you in implementing AI in your backend applications by demonstrating how to construct AI agents with tools like LangChain and Autogpt. You will gain practical knowledge about complex tools such as TensorFlow, Keras, and reinforcement learning algorithms, enabling you to develop autonomous AI agents and multimodal apps that enhance your current projects and elevate your technical skills.
As a business person, the Advanced Certification in Applied Gen AI and Agentic Systems will equip you with the hands-on skills to integrate generative AI and autonomous agents into your business processes. You'll gain skills in building AI models with ChatGPT, LangChain, and reinforcement learning, allowing you to develop tailored AI-powered applications that enhance your business functions, drive efficiency, and enhance customer experience.
After completing the certification, you will gain practical experience in developing AI-powered solutions with tools such as LangChain, AutoGPT, and Crewai. You will also learn to fine-tune large language models (LLMs), utilise reinforcement learning, and create multimodal AI systems using technologies like GPT-4V and CLIP. The hands-on training enables you to apply these skills to real-world scenarios and develop autonomous AI agents that can reason and solve problems.
Completing this certification broadens many career opportunities in the fast-expanding field of AI. Graduates are well-suited for roles such as AI Engineer, Data Scientist, Machine Learning Specialist, and AI Solutions Architect. With the skills to design, develop, and deploy advanced AI applications, you can work across various sectors, including IT, finance, healthcare, and more, enhancing job prospects and future career growth.
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