AI Model Design, Development & Robotics

Course overview:

This course covers how AI systems are designed, trained, fine-tuned, deployed, and integrated into intelligent machines. Participants progress from machine learning and deep learning fundamentals through fine-tuning, MLOps deployment, and AI robotics, including computer vision and reinforcement learning.

Course Content

Module 1: Machine Learning Fundamentals

Supervised vs. unsupervised learning

Model evaluation basics

Module 2: Deep Learning Foundations

Neural network basics

Backpropagation

Module 3: Neural Network Architectures

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Module 4: Transformers

Attention mechanisms

Transformer architecture

Module 5: Diffusion Models

Generative image model overview

Diffusion model use cases

Module 6: Data Engineering for ML

Data pipelines

Feature engineering

Module 7: Model Training

Training loops

Hyperparameter tuning

Module 8: Fine-Tuning

Transfer learning

Fine-tuning workflows

Module 9: LoRA & PEFT

Parameter-efficient fine-tuning techniques

Module 10: Model Evaluation

Evaluation metrics

Bias detection

Validation strategies

1/1

Module 11: MLOps & Deployment I

Docker fundamentals

Model serving

Module 12: MLOps & Deployment II

Monitoring

Cloud deployment (AWS/Azure/GCP)

Module 13: AI Robotics I

Robotics fundamentals

Sensor systems

Computer vision

Module 14: AI Robotics II

Reinforcement learning

Autonomous robotics

Capstone project work

Assessments and Projects:

Weekly quizzes and lab exercises

Lab: Fine-Tuned Domain LLM

Lab: Computer Vision System

Final Project: AI Robotics Simulation

Staffing Support

Resume Preparation
Mock Interview Preparation
Phone Interview Preparation
Face to Face Interview Preparation
Project/Technology Preparation
Internship with internal project work
Externship with client project work

Our Salient Features:

Hands-on Labs and Homework
Group discussion and Case Study
Course Project work
Regular Quiz / Exam
Regular support beyond the classroom
Students can re-take the class at no cost
Dedicated conf. rooms for group project work
Live streaming for the remote students
Video recording capability to catch up the missed class