Implementing Machine Learning Workflow with RapidMiner

In this course, you will learn how you can develop your machine learning workflow using RapidMiner Studio, a data science platform for data preparation, machine learning, and predictive model deployment.
Course info
Level
Intermediate
Updated
Dec 8, 2020
Duration
2h 23m
Table of contents
Course Overview
Implementing Machine Learning Models with RapidMiner Studio
Using JSAT to Implement Machine Learning Models
Using DJL to Implement Machine Learning Models
Description
Course info
Level
Intermediate
Updated
Dec 8, 2020
Duration
2h 23m
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Description

RapidMiner Studio provides an integrated development environment for data visualization, data preparation, machine learning, and deployment. In this course, Implementing Machine Learning Workflow with RapidMiner, you will get an overview of how you can use drag-n-drop operators to build and train machine learning models.

First, you will get introduced to RapidMiner studio, which is a no-code technology to develop your machine learning workflow. You will perform exploratory data analysis using RapidMiner, build linear regression models, evaluate models using cross-validation, and perform feature selection and normalization of input data, without writing a single line of code.

Next, you will explore a native Java library for traditional machine learning models. The Java Statistical Analysis Tool, or JSAT library, is a pure Java library that allows you to train regression, classification, and clustering models. You will use JSAT to perform linear regression, perform classification using logistic regression and decision trees, perform clustering using k-means clustering, and deploy your model using the SpringBoot framework in a limited production environment.

Finally, you will see how you can use the Deep Java Library, or DJL, to train neural network models in Java. DJL provides a native Java API and can run your training on multiple backends such as Apache MXNet, TensorFlow, and PyTorch. You will also leverage transfer learning and use pre-trained models for image classification, image segmentation, and natural language processing.

When you are finished with this course, you will be able to use no-code technologies and native Java libraries to build and train machine learning models.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi. My name is Janani Ravi, and welcome to this course on Implementing Machine Learning Workflow with RapidMiner. A little about myself. I have a masters in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on a real‑time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high‑quality video content. In this course, you will first get introduced to RapidMiner Studio, which is a no‑code technology to develop your machine learning workflow. You will perform exploratory data analysis using RapidMiner, build linear regression models, evaluate models using cross‑validation, and perform feature selection and normalization of input data, all without writing a single line of code. Next, you will explore a native Java library for traditional ML models. The Java Statistical Analysis Tool or the JSAT library is a pure Java library, which allows you to train regression, classification, and clustering models. You will use JSAT to perform linear regression. You'll perform classification using logistic regression, as well as decision trees, and you'll perform clustering using k‑means clustering. You will then deploy your model using the SpringBoot Framework in a limited production environment. Finally, you will see how you can use the Deep Java Library or DJL to train neural network models in Java. DJL provides a native Java API and can run your training on multiple back ends, such as Apache MXNet, TensorFlow, or PyTorch. You will also leverage transfer learning and use pretrained models for image classification, image segmentation, and natural language processing. When you're done with this course, you will be able to use no‑code technologies, as well as native Java libraries to build and train machine learning models.