مقدمه ای بر یادگیری ماشین با KNIME

تکمیل شده 0/33
  • Open-source machine learning with KNIME
    00:50
  • Who is this course for?
    02:54
  • Why use an Analytics Workbench?
    03:23
  • Using CRISP-DM to evaluate tools
    04:06
  • Why choose KNIME?
    04:44
  • The KNIME Hub
    02:35
  • The KNIME interface
    01:52
  • Find case studies on the Examples Server
    02:33
  • Add thousands of nodes with Extensions
    03:13
  • Search and Help
    01:52
  • Database access with KNIME
    05:11
  • Accessing data
    03:12
  • File reader node
    03:57
  • Describe data and verify data quality
    04:09
  • Explore data: Scatterplot
    02:58
  • Explore data: Boxplot
    03:48
  • Merging with the Joiner node
    02:24
  • Aggregating with the GroupBy node
    01:37
  • Creating new variables with Construct
    03:24
  • Select data with Column Filter
    02:17
  • Balancing data with Row Sampling node
    03:18
  • Clean data with the Missing Value node
    02:46
  • Format with Cell Splitter
    03:37
  • Components in KNIME: AutoML and XAI
    06:04
  • KNIME modeling options
    07:05
  • Regression example
    09:17
  • Decision tree
    06:33
  • Decision tree: Scoring new data
    01:49
  • Python options in KNIME
    05:46
  • Certification in KNIME
    02:29
  • PMML
    03:44
  • R and GGPLOT2
    04:08
  • Next steps
    01:31
Open-source machine learning with KNIME
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