Advanced Certification in Applied Gen AI and Agentic Systems

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

4.85/5

5193 Enrolled

Overview

What will you learn from us:

Master deep learning techniques using Keras and TensorFlow
Build real-world NLP applications with ChatGPT and LangChain
Implement reinforcement learning using Q-learning and Grid-world agents
Develop AI agents using LangChain and AutoGPT frameworks
Create multimodal AI models using GPT-4V and CLIP
Fine-tune domain-specific LLMs for real-world applications

Learning Outcomes

After you complete your training, you will be able to:

Create AI agents with LangChain, AutoGPT and CrewAI tailored for practical real-world applications

Master NLP techniques using ChatGPT, LangChain and transformer-based frameworks for advanced language processing tasks

Apply reinforcement learning through Q-learning, PPO methods and Grid-world simulations to solve dynamic problems

Build multimodal AI solutions integrating GPT-4V, CLIP and text-to-image models for innovative outputs

Generate synthetic images and datasets using GANs such as DCGAN and CycleGAN for diverse applications

Deploy autonomous AI solutions utilising CrewAI and LangGraph to deliver real-time intelligent performance

Ready to get started?

Prerequisites

Before enrolling in the Advanced Certification in Applied Gen AI and Agentic Systems, the eligibility requirements are as follows:

Applicants should hold a B.E., B.Tech, M.E, M.Tech, M.Sc., MBA, or an equivalent master’s degree with at least 50% marks along with 1 year of professional work experience
Candidates with a BSc, BA, LLB, CA, or BBA must have secured a minimum of 65% marks and possess 2 years of relevant professional experience. For non-engineering graduates, a high school-level mathematics background is mandatory
Programming knowledge is required, with prior coding experience in Python being highly preferred

Overall ratings by our students

Upcoming sessions

Our Trainers

Learners Point has a reputation for high-quality training that makes a difference in people's lives. We undertake a practical and innovative approach to working closely with businesses to improve their workforce. Our expertise is wide-ranging with ample support from our expert trainers who are globally recognized and hold a diverse set of experiences in their field of expertise. We are proud of our instructors who take ownership of our distinctive and comprehensive training methodologies, help our students imbibe those with ease, and accomplish gracefully.

We at Learners Point believe in encouraging our students to embark upon a journey of lifelong learning and self-development, with the aid of our comprehensive and distinctive courses tailored to current market trends. The manifestation of our career-oriented approach is what we assure through a pleasant professional enriched environment with cutting-edge technology, and an outstanding while highly acknowledged training staff that uses up-to-date methodologies and quality course material. With our aim to mold professionals to be future leaders, our industry expert trainers provide the best in town mentorship to our students while endowing them with the thirst for knowledge and inspiring them to strive for professional and human excellence.

Learners Point Certificate

Earn a Course Completion Certificate, an official Learners Point credential that confirms that you have successfully completed a course with us.

Related courses

Curriculum

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, DALL·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, DALL·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 → tools → 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

Project Brief: Build and deploy an end-to-end autonomous system powered by LLMs, LangChain agents, external tools, memory and reasoning strategies.

Deliverables:

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 %)

Example Themes:

Meeting summarizer + action item generator
Business intelligence research assistant
Autonomous marketing campaign writer
Project planner with goal decomposition

Frequently asked questions

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 Certification in Applied Gen AI and Agentic Systems is designed for a wide range of candidates who want to build practical skills in Generative AI, LLM-based AI Agents and autonomous systems. Eligibility is flexible, focusing on knowledge, interest and career goals. Suitable candidates include:

Professionals aiming to lead AI projects or innovation
Students from engineering, computer science, or related fields seeking AI expertise
Entrepreneurs exploring AI-driven solutions for their ventures
Individuals with basic programming knowledge who want hands-on experience with tools like ChatGPT, LangChain, AutoGPT and CrewAI

Yes, the Advanced Certification in Applied Gen AI and Agentic Systems includes a dedicated capstone project designed to integrate all core concepts. You will apply skills in Generative AI, LLM-based AI Agents and autonomous systems to develop real-world applications, which simulates professional AI workflows.

These projects allow you to showcase reasoning, planning, memory and tool integration capabilities using tools like ChatGPT, LangChain, AutoGPT and CrewAI. Completing the capstone also contributes to a strong portfolio for career opportunities.

After completing the Advanced Certification in Applied Gen AI and Agentic Systems, you can pursue roles that utilise AI, LLMs and autonomous systems expertise. Key positions include AI Engineer, Machine Learning Specialist, Data Scientist and Generative AI Developer, where you design, implement and optimise intelligent solutions for businesses.

These roles span industries such as technology, healthcare, finance and logistics. Certified candidates also gain cross-industry mobility, leadership prospects and opportunities to contribute to cutting-edge AI projects globally.

Through the Advanced Certification in Applied Gen AI and Agentic Systems, you will gain hands-on experience using industry-relevant tools to build, test and deploy generative AI models. These tools are integral to mastering LLM-based AI agents and autonomous systems. These tools are:

ChatGPT – for conversational AI and language model integration
LangChain – to create chain-based AI workflows
AutoGPT – for autonomous agent development
CrewAI – to manage multi-agent systems and tool integration

In the Advanced Certification in Applied Gen AI and Agentic Systems, AutoGPT plays a key role in teaching autonomous agent development. You will learn how to design AI agents capable of performing complex tasks with minimal human intervention.

By working with AutoGPT, you gain practical experience in integrating tools, managing workflows and building self-directed AI applications. This in turn prepares you for real-world generative AI projects and advanced automation roles in the industry.

Yes, earning the Advanced Certification in Applied Gen AI and Agentic Systems allows you to build a professional portfolio showcasing your skills and projects. This portfolio shows your expertise in generative AI and autonomous agent systems that makes you more attractive to employers. This includes the following:

Capstone AI projects demonstrating real-world problem solving
GitHub repository of completed generative AI and agentic system models
Samples of LLM-based AI agent implementations
Practical applications using ChatGPT, LangChain, AutoGPT and CrewAI

Yes, you will be able to earn the Advanced Applied Gen AI and Agentic Systems certification if you are from different GCC location. We deliver the same industry-recognised Advanced Applied Gen AI and Agentic Systems training in different GCC countries for professionals globally. These regions are: