Introduction to Machine Learning with KNIME

Introduction to Machine Learning with KNIME
Introduction to Machine Learning with KNIME
English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 41m | 324 MB

KNIME is an open-source workbench-style tool for predictive analytics and machine learning. It is highly compatible with numerous data science technologies, including R, Python, Scala, and Spark. With KNIME, you can produce solutions that are virtually self-documenting and ready for use. These reasons and more make KNIME one of the most popular and fastest-growing analytics platforms around. In this course, expert Keith McCormick shows how KNIME supports all the phases of the Cross Industry Standard Process for Data Mining (CRISP-DM) in one platform. Get up and running quickly—in 15 minutes or less—or stick around for the more in-depth training covering merging and aggregation, modeling, and data scoring. Plus, learn how to increase the power of KNIME with extensions and integrate R and Python.

Topics include:

  • Why use a workbench
  • Why choose KNIME?
  • Adding KNIME nodes with extensions
  • Accessing data
  • Exploring data statistically and visually
  • Merging and aggregating data in KNIME
  • Modeling in KNIME
  • Scoring new data
  • Combining KNIME with R and Python
Table of Contents

1 Open-source machine learning with KNIME
2 Who is this course for
3 Why use an Analytics Workbench
4 Using CRISP-DM to evaluate tools
5 Why choose KNIME
6 The KNIME interface
7 Find case studies on the Examples Server
8 Add thousands of nodes with Extensions
9 Search and Help
10 Accessing data
11 File reader node
12 Describe data and verify data quality
13 Explore data Scatterplot
14 Explore data Boxplot
15 Merging with the Joiner node
16 Aggregating with the GroupBy node
17 Creating new variables with Construct
18 Select data with Column Filter
19 Balancing data with Row Sampling node
20 Clean data with the Missing Value node
21 Format with Cell Splitter
22 KNIME modeling options
23 Regression example
24 Decision tree
25 Decision tree Scoring new data
26 PMML
27 R and GGPLOT2
28 Other options
29 Next steps