In this hands-on tutorial you will graphically build and evaluate machine learning models by using the SPSS Modeler flow feature in Watson Studio.
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This tutorial consists of 6 parts, you can start with part I or any other part, however, the necessary environment is set up in part I.
Part I – data visualization, preparation, and transformation
Part II – build and evaluate machine learning models by using AutoAI
Part III – graphically build and evaluate machine learning models by using SPSS Modeler flow
Part IV – set up and run Jupyter Notebooks to develop a machine learning model
Part V – deploy a local Python app to test your model
Part VI – monitor your model with OpenScale
To learn more about Jupyter and Jupyter Notebooks click here.
Definitions from jupyter.org for Project Jupyter and Jupyter Notebooks are:
“Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages.”
“The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.”
In your Watson Studio project you can also add new Notebooks and start developing your machine learning models.
To access the complete tutorial go to this GitHub Repository.