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 you will master with 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
Build practical AI agents using LangChain, AutoGPT, and CrewAI for real-world use cases
2
Learn advanced NLP techniques with ChatGPT, LangChain, and transformer-based models for complex language tasks
3
Use reinforcement learning with Q-learning, PPO, and Grid-world environments to solve adaptive challenges
4
Create innovative multimodal AI systems by combining GPT-4V, CLIP, and text-to-image models
5
Produce synthetic data and visuals using GANs like DCGAN and CycleGAN for varied applications
6
Deploy autonomous AI systems with CrewAI and LangGraph to enable responsive, intelligent behaviors in real time
Before enrolling in the Advanced Certification in Applied Gen AI and Agentic Systems, the eligibility requirements are as follows:
Overall ratings by our students
Our Advanced Certification in Applied Gen AI and Agentic Systems in Qatar is a practical, industry-focused program that teaches you to design intelligent AI systems. It covers deep learning, generative AI, NLP, and reinforcement learning. Learners work on real projects using tools like LangChain, AutoGPT, and transformers, helping them build autonomous agents that solve real-world problems.
You do not need a technical background. Our Applied Gen AI and Agentic Systems course helps you understand how Gen AI and agentic systems work so you can lead AI-powered product strategies. Participants learn how to map use cases, evaluate models like GPT-4V, and align AI features with customer value. We help you become a stronger cross-functional leader in AI product development.
Our Advanced Certification in Applied Gen AI and Agentic Systems in Qatar gives you startup-ready AI skills. We include everything from building MVPs with AutoGPT to fine-tuning LLMs for niche use cases. Participants learn how to integrate APIs, design intelligent agents, and even develop multimodal prototypes. These capabilities will not only set your startup apart but also attract investors looking for innovative AI-based solutions.
This training is ideal for IT professionals, tech students, and entrepreneurs interested in building applied AI systems. We help you learn how to develop intelligent agents, optimize large language models, and integrate tools like Zapier or Google Search. This program gives you the hands-on skills and project experience to excel in the AI industry.
By the end of the course, individuals know how to build and deploy AI solutions using platforms like LangChain, CrewAI, and AutoGPT. You gain hands-on experience with LLM fine-tuning, reinforcement learning, and multimodal AI tools like GPT-4V and CLIP. These skills allow you to create intelligent agents capable of reasoning, automation, and complex problem-solving.
Graduates are prepared for high-demand roles such as:
- AI Engineer
- Machine Learning Specialist
- Data Scientist
- AI Solutions Architect
- AI Researcher
Learn now, pay later
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