Overview
This training program introduces participants to Machine Learning integrated with Agentic AI, focusing on building intelligent agents capable of autonomous planning, reasoning, and task execution. It covers real-world applications across industries such as healthcare, finance, automation, and customer service. Through hands-on exercises and case studies, participants gain practical skills to develop adaptive, decision-making systems that extend beyond traditional ML.
Our Trainers
Mr. Gaurav Nema
- Present: C.T.O. at INNOBIMB INFOTECH Pvt Ltd.
- Solution Architect and Consultant in CRISP.
- Solution Architect and Data Analyst in RGPV.
- Master AI Trainer in RCVP Noronha Academy of Administration - Madhya Pradesh.
- Software Architect in Different Software Companies o Technical Expert from last 15 Years.
- Training to faculties of RGPV Artificial Neural Networks.
- Training to faculties of RGPV Machine Learning.
Modules
- Build Smart AI Agents: Learn how to design AI systems that think, plan, and act autonomously
- Hands-on ML + Agent Integration: Combine machine learning models with agent-based frameworks
- Real-world Use Cases: Applications in chatbots, automation, recommendation systems, and more
- Prompt Engineering & Decision Making: Train agents to reason and execute multi-step tasks
- Mini Project Experience: Develop a working Agentic AI solution by the end of the course.
Outcomes
By the end of this course, participants will be able to:
- Understand core concepts of Machine Learning and Agentic AI, including how intelligent agents operate and interact with environments.
- Design and develop AI agents capable of autonomous planning, reasoning, and task execution using ML models.
- Apply ML techniques to solve real-world problems across domains such as healthcare, finance, automation, and customer service.
- Integrate decision-making capabilities into AI systems to enable adaptive and goal-oriented behavior.
- Utilize tools and frameworks for building and deploying agent-based AI applications.
- Analyze case studies and implement solutions through hands-on projects, demonstrating practical understanding of Agentic AI systems.
- Evaluate performance and limitations of ML-driven agents and propose improvements for real-world deployment.
