- Module 1: IBM Watson Studio
- Module 2: Lab 1: Create a Watson Studio Project
- Module 3: Lab 2: Build a Machine Learning Model
- Module 4: Lab 3: Create predictions in a Node-RED application
- Module 5: Lab 4: Create multiclass classification models
- Module 6: Lab 5: Create UIs and integrate visual recognition
- In this course, you'll use IBM Watson Studio to build classification models to predict (identify) animal sounds and use IBM Watson Visual Recognition to identify images of those animals
- You'll learn how best to gather and prepare data, create and deploy models, deploy and test a signal processing application, create models with binary and multiclass classifications, and display the predictions on a web page that you create by using Node-RED
- When you finish this course, you should know how to:
- Prepare data so that it can be consumed by machine learning models
- Build a binary classification model that can predict which animal (dogs and cats) is making a specific sound
- Build a multiclass classification model to detect whether a birdsong is from a bird from a specific order and view the confidence level of that prediction
- Make predictions on audio files by using a Node-RED application built as a web page
- Create an application in Node-RED that integrates the Watson Visual Recognition service with your machine learning model to recognize images of cats and dogs
- Lab quizzes(60%, no time limit) + Final Exam(40%, 1-hour time limit)
