Course details

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 techniques and there are various algorithms available for these tasks. Explore their application in Pandas ML.



Expected Duration (hours)
1.1

Lesson Objectives

Machine & Deep Learning Algorithms: Data Preperation in Pandas ML

  • 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