Machine & Deep Learning Algorithms: Data Preparation in Pandas ML

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

  • 2m 16s
  • 6m 26s
    In this video, you will learn how to load data from a CSV file into a Pandas dataframe and prepare the data for training a classification model. FREE ACCESS
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    3.  Training and Evaluating Models in scikit-learn
    6m 56s
    Find out how to use the scikit-learn library to build and train a LinearSVC classification model. Then, evaluate its performance using the available model evaluation functions. FREE ACCESS
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    4.  Introducing the Pandas ML ModelFrame
    5m 48s
    Learn how to install Pandas ML, then define and configure a ModelFrame. FREE ACCESS
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    5.  Training and Evaluating Models in Pandas ML
    7m 28s
    During this video, you will learn how to compare training and evaluation in Pandas ML with the equivalent tasks in Scikit-learn. FREE ACCESS
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    6.  Preparing Data for Regression
    7m 32s
    In this video, you will use Pandas for feature extraction and one-hot encoding, load its contents into a DataFrame, and initialize and train a linear regression model. FREE ACCESS
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    7.  Evaluating Regression Models
    8m 21s
    During this video, you will learn how to evaluate a regression model using metrics such as r-square and mean squared error, and visualize its performance using Matplotlib. FREE ACCESS
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    8.  Preparing Data for Clustering
    4m 41s
    In this video, you will work with ModelFrames to extract features and label encoding. FREE ACCESS
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    9.  The K-Means Clustering Algorithm
    7m 16s
    In this video, you will learn how to configure and build a clustering model using the K-Means algorithm, and analyze data clusters to determine characteristics that are unique to them. FREE ACCESS
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    10.  Exercise: Regression, Classification, and Clustering
    6m 57s
    In this video, you will learn how to 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. FREE ACCESS

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