Machine Learning Rapid Prototyping with IBM Watson Studio

IBM via Coursera

Coursera based on 15 ratings

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Overview

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studios Auto AI experiment tool, explaining the underlying technology at...

Syllabus

Building a Rapid Prototype with Watson Studio AutoAI

In this module, you'll learn about the developing landscape of AutoAI technologies. You'll also become familiar with the Watson Studio platform in order to be able to perform your own AutoAI Experiments. After observing the AutoAI tool build prototypes for two use cases, you will try out the tool for yourself to build additional prototypes.

Automated Data Preparation and Model Selection

In this module, you will learn about the automated data preparation techniques performed by AutoAI and get a chance to experiment with different settings for data preprocessing in the AutoAI-generated Python notebook. You'll also learn about the procedure for automated model selection and experiment using different models on the datasets.

Automated Feature Engineering and Hyperparameter Optimization

In this module, you will learn about the algorithm for automated feature engineering and perform some exploratory data analysis to try to understand why the algorithm performed particular feature transformations. You'll also learn about sophisticated methods for optimizing hyperparameters and explore hyperparameter tuning on the datasets using the AutoAI-generated Python notebook.

Evaluation and Deployment of AutoAI-generated Solutions

In this module, you will evaluate prototypes using the different evaluation metrics calculated by the AutoAI tool. You will also deploy the prototype for testing using the Watson Machine Learning API.