KNIME with Python and R for Machine Learning培训

课程编码

knimepyr

课程时长

14 小时 通常来说是2天,包括中间休息。

要求

  • Experience with Python
  • R experience

Audience

  • Data Scientists

课程概览

KNIME is an open source data analytical software for integrating machine learning and data mining through data pipelines. With Python and R, users are able to extend KNIME in its capabilities for data analytics and machine learning.

This instructor-led, live training (online or onsite) is aimed at data scientists who wish to program in Python and R for KNIME.

By the end of this training, participants will be able to:

  • Plan, build, and deploy machine learning models in KNIME.
  • Implement end to end data science projects.
  • Make data driven decisions for operations.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.

证书

KNIME

KNIME

NobleProg and KNIME design, build and deliver end-to-end advanced analytics solutions that are customized to each customer's business needs.

By combining KNIME's leading open solution for data driven innovation with NobleProg's domain and technical expertise in analytics, we help our customers reduce costs and gain data-driven insights for better business outcomes.

课程大纲

Introduction

Getting Started with Knime

  • What is KNIME?
  • KNIME Analytics
  • KNIME Server

Machine Learning

  • Computational learning theory
  • Computer algorithms for computational experience

Preparing the Development Environment

  • Installing and configuring KNIME

KNIME Nodes

  • Adding nodes
  • Accessing and reading data
  • Merging, splitting, and filtering data
  • Grouping and pivoting data
  • Cleaning data

Modeling

  • Creating workflows
  • Importing data
  • Preparing data
  • Visualizing data
  • Creating a decision tree model
  • Working with regression models
  • Predicting data
  • Comparing and matching data

Learning Techniques

  • Working with random forest techniques
  • Using polynomial regression
  • Assigning classes
  • Evaluating models

Summary and Conclusion

客户评论

★★★★★
★★★★★

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