Description

This course is intended for: Novice data analysts who need to understand the how the machine learning algorithms work, and when they are best applied to their data Experienced data scientists who need to know how to use Vertica's built-in machine learning algorithms

Machine learning is used to discover trends, uncover patterns, peel back layers and detect relationships over large volumes of data. In order to build accurate predictive models, issues in your data must first be addressed. During this course, you will learn: Adjusting the scales of values in different columns using NORMALIZE() Fill in missing values using IMPUTE() Find and eliminate outlying data points using DETECT_OUTLIERS() Even out skewed class distributions using BALANCE() NOTE: This module is a subset of the VT160 Predictive Analytics Using Machine Learning course. If you have already purchased that course, you do not need to also purchase this module.

Audience Summary

This course is intended for:

• Novice data analysts who need to understand the how the machine learning algorithms work, and when they are best applied to their data

• Experienced data scientists who need to know how to use Vertica's built-in machine learning algorithms


Course Topics

  • Introduction to Machine Learning
  • Data Preparation
  • Regression Algorithms
  • Classification Algorithms
  • Clustering Algorithms
  • Model Management

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Details

Course
VT160- 1
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Course outline

Data Preparation

• Describe the data preparation process

• Prepare data using the following functions:

o Normalize data scales

o Impute missing values o Identify and remove outlying values

o Smooth class representation within a column

• Create training and testing data sets

Languages
English
VT160- 2
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Course outline

Regression Algorithms

Build and evaluate the following model types:

  • Linear regression
  • Random forest
  • Support vector machines
Languages
English
VT160- 3
More info Less info
Course outline

Classification Algorithms

Build and evaluate the following model types:

  • Logistic regression
  • Random forest
  • Support vector machines
  • Naïve Bayes
Languages
English

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release-rel-2022-1-1-4548 | Wed Jan 19 06:11:08 PST 2022