Machine & Deep Learning Algorithms: Data Preparation in Pandas ML

Machine Learning    |    Beginner
  • 10 Videos | 1h 3m 41s
  • Includes Assessment
  • Earns a Badge
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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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 16s
    UP NEXT
  • Playable
    2. 
    Data Preparation in scikit-learn
    6m 26s
  • Locked
    3. 
    Training and Evaluating Models in scikit-learn
    6m 56s
    FREE ACCESS
  • Locked
    4. 
    Introducing the Pandas ML ModelFrame
    5m 48s
    FREE ACCESS
  • Locked
    5. 
    Training and Evaluating Models in Pandas ML
    7m 28s
    FREE ACCESS
  • Locked
    6. 
    Preparing Data for Regression
    7m 32s
    FREE ACCESS
  • Locked
    7. 
    Evaluating Regression Models
    8m 21s
    FREE ACCESS
  • Locked
    8. 
    Preparing Data for Clustering
    4m 41s
    FREE ACCESS
  • Locked
    9. 
    The K-Means Clustering Algorithm
    7m 16s
    FREE ACCESS
  • Locked
    10. 
    Exercise: Regression, Classification, and Clustering
    6m 57s
    FREE ACCESS

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