Machine & Deep Learning Algorithms: Data Preparation in Pandas ML

Machine Learning    |    Beginner
  • 10 Videos | 1h 3m 41s
  • Includes Assessment
  • Earns a Badge
Likes 62 Likes 62
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

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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