Python AI, ML, DL & Robotics Development (Advanced)

This comprehensive self-paced course spans 4 weeks and is designed for experienced developers and engineers eager to delve into the cutting-edge world of AI-powered robotics. Building upon foundational and intermediate knowledge, participants will explore advanced topics in deep learning, machine learning engineering, and robotics integration.

Week 1: Advanced Deep Learning & Model Optimization

• Custom neural network architecture design, focusing on CNN, GAN, and Transformer models.
• Advanced techniques in transfer learning and model stacking for improved performance.
• Hyperparameter tuning utilizing automated tools like Optuna and Ray Tune.
• Strategies for model compression, quantization, and deployment targeted at edge devices.
• Assignment: Build and optimize a hybrid deep learning model.

Week 2: AI for Robotics – Real-World Integration

• Robotic system simulation employing reinforcement learning through platforms like OpenAI Gym and Stable Baselines3.
• Exploration of multi-agent robotics and swarm intelligence, including cooperative reinforcement learning techniques.
• Integration of advanced AI modules with ROS2 for enhanced robotic functionalities.
• Sensor fusion techniques involving LIDAR, vision, IMU, and GPS for effective navigation.
• Hands-on: Implement robotic navigation using AI control in simulated environments.

Week 3: Production-Ready AI & ML Engineering

• In-depth study of advanced MLOps practices, including model versioning, CI/CD pipelines, and monitoring with MLflow.
• Creating scalable AI pipelines that incorporate both data streaming and batch processing techniques using Kafka and Apache Beam.
• Understanding and applying robust model interpretability methods such as SHAP and LIME.
• Ensuring secure and ethical deployment of robotics and AI systems in real-world applications.
• Project: Deploy a complete end-to-end AI-driven robotics application.

Week 4: Capstone & Cutting-Edge Applications

• Exploration of conversational AI and the application of language models within robotics control contexts.
• Vision-language integration techniques utilizing CLIP and multimodal learning frameworks.
• Designing autonomous systems that implement planning and SLAM in complex environments.
• Capstone: Design, deploy, and document an advanced AI-robotics system.

Upon completion of this course, participants will receive a recognized certificate from the Academy, demonstrating their readiness for roles that leverage advanced AI, deep learning, and robotics in various industries, research sectors, and startups. This project-driven capstone aims to equip students with the necessary skills to tackle challenges in deploying, monitoring, and scaling intelligent robotic systems ethically and effectively.