• Modern Artificial Intelligence with Zero Build, train and deploy AI models to detect people emotions using Google Teachable Machine.
• Explain the difference between learning rate, epochs, batch size, accuracy and loss.
• Predict Insurance Premium using Customer Features such as age, smoking habit and geo-location using AWS AI Autopilot.
• Build, train and deploy advanced AI to detect cardiovascular disease using Data Robot AI
• Leverage the power of AI to recognize food types using Data Robot AI
• Develop an AI model to detect and classify chest disease using X-Ray chest data using Google Teachable Machines
• Evaluate trained AI models using various KPIs such as confusion matrix, classification accuracy, and error rate
• List the various advantages of transfer learning and know when to properly apply the technique to speed up training process
• Understand the theory and intuition behind residual networks, a state-of-the-art deep neural networks that are widely adopted in business, and healthcare
• Learn how to train multiple AI models based on XG-Boost, Artificial Neural Networks, Random Forest Classifiers and compare their performance in Data Robot
• Understand the impact of classifier threshold on False Positive Rate (Fallout) and True Positive Rate (Sensitivity)
• Learn how to use Sage Maker Studio Auto ML tool to build, train and deploy AI/MLX models which requires almost zero coding experience
• Differentiate between various regression models KPIs such as R2 or coefficient of determination, Mean absolute error, Mean Squared error, and Root Mean Squared Error
• Modern Artificial Intelligence with Zero Build, train and deploy XGBoost-based algorithm to perform regression tasks using AWS Sage Maker Autopilot.

Requirements