Beginners guide to artificial intelligence

Understand the role of AI in robotics, including perception, decision-making, and autonomous actions.

Short and Complete AI, Machine Learning & Deep Learning Course for Professionals, Managers and Business Leaders

Build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant

Teach a Machine to Learn to Recognize Images, Audio and Poses

Learn about the attention mechanism in neural networks and its applications in AI tasks using Google Cloud.

Dive into autoencoders and regularization techniques. Learn how to build and implement autoencoders for unsupervised learning tasks, improving data representation and reducing overfitting.

Learn how to automate repetitive tasks using Large Language Models (LLMs). Streamline workflows, create bots, and enhance productivity with AI-driven automation.

Learn how to implement automated inspection systems using computer vision techniques, focusing on image processing and analysis for quality control.

This learning path is a brief introduction to a new AI paradigm called Machine Teaching for Autonomous AI. Machine Teaching uses the knowledge of subject matter experts to teach AI. It integrates known control methods for stable control, Machine Learning for advance perception, and Deep Reinforcement Learning for learning strategies and human-like decision making. It's AI integrated in industrial processes without disruption and providing real business value. It's validated in simulation, explainable that your experts can validate. Machine Teaching for Autonomous AI stores expert operators' skill set, enhancing and homogenizing expert operators' best performance, and/or efficiently training novices on the job when deployed as a Decision Support tool. It helps your company achieve new levels of optimization for competitiveness, profitability, and sustainability. It provides a robust innovation path for all areas of industrial processes and business with real ROI. At the end of this learning path, you'll be able to:

Learn how to prevent security vulnerabilities in generative AI applications. Understand common exploits and implement best practices to protect your AI-driven systems from potential risks.

Engage in a hands-on experience to learn reinforcement learning through autonomous racing simulations with AWS DeepRacer.