Implementing Machine Learning Workflow with RapidMiner

سرفصل های دوره

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.

1. Course Overview

1. Course Overview

02. Implementing Machine Learning Models with RapidMiner Studio

01. Version Check

02. Prerequisites and Course Outline

03. Introducing RapidMiner

04. Demo - Download and Setup RapidMiner

05. Demo - Setting up a Repository and Importing Data

06. Demo - Exploring the Dataset

07. Demo - Build and Evaluate a Linear Regression Model

09. Demo - Perform Attribute Selection

10. Demo - Evaluate a Model Using Cross-validation

11. Demo - Assign Roles and Perform Attribute Selection

12. Demo - Train a Model with Normalized Data

03. Using JSAT to Implement Machine Learning Models

01. Introducing JSAT

02. Demo - Getting Set up with a Maven Project

03. Demo - Loading and Exploring Data

04. Demo - Building and Training a Regression Model

05. Demo - Evaluating a Regression Model

06. Demo - Training and Evaluating a Ridge Regression Model

09. Demo - Performing Clustering and Evaluating Clustering Models

10. Demo - Serializing and Deserializing Trained Models

11. Demo - Making Predictions Using a Deployed Model

04. Using DJL to Implement Machine Learning Models

01. Introducing DJL

02. Brief Overview of Neural Networks

03. Demo - Setting up the Maven Project and Dependencies

05. Demo - Training the Image Classification Model

06. Demo - Performing Predictions Using the Classification Model

07. Brief Overview of Transfer Learning

08. Demo - Using a Pretrained Model for Image Classification

09. Demo - Using a Pretrained Model for Image Segmentation

10. Introducing Google BERT

11. Demo - Answering Questions with Google BERT

12. Summary and Further Study

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تولید کننده: PluralSight