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. Tanishq Vyas
- AI/ML Instructor & Developer with 2+ years of hands-on teaching experience.
- Guided 1,500+ students through the world of Artificial Intelligence and Machine Learning — from foundational concepts to real-world applications.
- Passionate about making complex tech simple, practical, and accessible.
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.
-
