10 Days of No Code Artificial Intelligence Bootcamp

Build, train, test and deploy 10 AI/ML models in 10 days without writing any code.

Build, train, test and deploy AI models to classify fashion items using Google Teachable Machine.

Visualize State-of-the-Art Artificial Intelligence Models Using Tensorspace JS, Google Tensorflow Playground and Ryerson 3D CNN Visualizations.

Explain the difference between learning rate, epochs, batch size, accuracy, and loss.

Build, train and deploy advanced AI to detect Diabetic Retinopathy disease using DataRobot AI.

Leverage the power of AI to solve regression tasks and predict used car prices using DataRobot AI.

Evaluate trained AI models using various KPIs such as confusion matrix, classification accuracy, and error rate.

Understand the theory and intuition behind Residual Neural Networks (ResNets), a state-of-the-art deep NNs that are widely adopted in several industries.

Understand the impact of classifier threshold on False Positive Rate (Fallout) and True Positive Rate (Sensitivity).

Predict employee attrition based on their features such as employee engagement, distance from home, job satisfaction using DataRobot AI.

Develop an AI model to detect face masks using Google Teachable Machines.

Build, train and deploy XGBoost-based algorithm to perform regression tasks using AWS SageMaker Autopilot.

Learn how to transfer knowledge from a pre-trained Artificial Neural Network to a new network using transfer learning strategy.

Learn how to train multiple AI models based on XG-Boost, Artificial Neural Networks, Random Forest Classifiers and compare their performance in DataRobot.

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 and Mean Squared error.

Learn how to build, train, test and deploy advanced machine learning classification models using Google Vertex AI.

Understand how to leverage the power of AI/ML to predict bank customers credit card default using their features such as interest rates and loan purpose

Learn how to create a new dataset using Google Vertex AI Develop and manage experiments using Google Vertex AI.

Understand the theory, intuition, and mathematics behind simple and multiple linear regression and differentiate between various regression models KPIs.

Deploy the best model after the hyperparameters optimization job is complete and Learn how to assess feature importance and explain model predictions.

Deploy and monitor AI/ML models and create AI/ML applications with Google Vertex AI.