Modern Artificial Intelligence with Zero Coding

What Will You Learn?

• 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 DataRobot AI
• Leverage the power of AI to recognize food types using DataRobot 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 DataRobot
• Understand the impact of classifier threshold on False Positive Rate (Fallout) and True Positive Rate (Sensitivity)
• Learn how to use SageMaker Studio AutoML tool to build, train and deploy AI/Ml 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
• Build, train and deploy XGBoost-based algorithm to perform regression tasks using AWS SageMaker Autopilot