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Machine & Deep Learning Algorithms: Data Preperation in Pandas ML

Machine & Deep Learning Algorithms: Data Preperation in Pandas ML


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

Classification, regression, and clustering are some of the most commonly used machine learning (ML) techniques and there are various algorithms available for these tasks. In this 10-video course, learners can explore their application in Pandas ML. First, examine how to load data from a CSV (comma-separated values) file into a Pandas data frame and prepare the data for training a classification model. Then use the scikit-learn library to build and train a LinearSVC classification model and evaluate its performance with available model evaluation functions. You will explore how to install Pandas ML and define and configure a ModelFrame, then compare training and evaluation in Pandas ML with equivalent tasks in scikit-learn. Learn how to build a linear regression model by using Pandas ML. Then evaluate a regression model by using metrics such as r-square and mean squared error, and visualize its performance with Matplotlib. Work with ModelFrames for feature extraction and encoding, and configure and build a clustering model with the K-Means algorithm, analyzing data clusters to determine unique characteristics. Finally, complete an exercise on regression, classification, and clustering.



Expected Duration (hours)
1.1

Lesson Objectives

Machine & Deep Learning Algorithms: Data Preperation in Pandas ML

  • Course Overview
  • load data from a CSV file into a Pandas dataframe and prepare the data for training a classification model
  • use the scikit-learn library to build and train a LinearSVC classification model and then evaluate its performance using the available model evaluation functions
  • install Pandas ML and then define and configure a ModelFrame
  • compare training and evaluation in Pandas ML with the equivalent tasks in scikit-learn
  • use Pandas for feature extraction and one-hot encoding, load its contents into a ModelFrame, and initialize and train a linear regression model
  • evaluate a regression model using metrics such as r-square and mean squared error and visualize its performance using Matplotlib
  • work with ModelFrames for feature extraction and label encoding
  • configure and build a clustering model using the K-Means algorithm and analyze data clusters to determine characteristics that are unique to them
  • distinguish between the use of scikit-learn and Pandas ML when training a model and identify some of the metrics used to evaluate a model
  • Course Number:
    it_dsmdladj_03_enus

    Expertise Level
    Beginner