Building ML Training Sets: Introduction

Machine Learning    |    Beginner
  • 10 videos | 1h 9m 21s
  • Includes Assessment
  • Earns a Badge
There are numerous options available to scale and encode features and labels in data sets to get the best out of machine learning (ML) algorithms. In this 10-video course, explore techniques such as standardizing, nomalizing, and one-hot encoding. Learners begin by learning how to use Pandas library to load a data set in the form of a CSV file and perform exploratory analysis on its features. Then use scikit-learn's Binarizer to transform the continuous data in a series to binary values; apply the MiniMaxScaler on a data set to get two similar columns to have the same range of values; and standardize multiple columns in data sets with scikit-learn's StandardScaler. Examine differences between the Normalizer and other scaling techniques, and learn how to represent values in a column as a proportion of the maximum absolute value by using the MaxAbScaler. Finally, discover how to use Pandas library to one-hot encode one or more features of your data set and distinguish between this technique and label encoding. The concluding exercise involves building ML training sets.

WHAT YOU WILL LEARN

  • Use the pandas library to load a dataset in the form of a csv file and perform some exploratory analysis on its features
    Transform the continuous data in a series to binary values by using scikit-learn's binarizer
    Apply the minmaxscaler on a dataset to get two similar columns to have the same range of values
    Standardize multiple columns in your dataset using scikit-learn's standardscaler
    Distinguish between the normalizer and other scaling techniques and apply this scaler on the continuous features of a dataset
  • Represent the values in a column as a proportion of the maximum absolute value by using the maxabsscaler
    Apply label encoding on the features and target in your dataset and recognize its limitations when applied on input features
    Use the pandas library to one-hot encode one or more features of your dataset and distinguish between this technique and label encoding
    Transform a continuous series into a categorical (binary) one, distinguish between normalization and other scaling techniques, score each product as a proportion of the top product’s sales, and encode the ”vehicletype” field which contains values [“hatchback”, “sedan”, “suv”]

IN THIS COURSE

  • 2m 37s
  • 9m 7s
    In this video, you will use the Pandas library to load a dataset in the form of a CSV file and perform some exploratory analysis on its features. FREE ACCESS
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    3.  The Binarizer
    6m 20s
    In this video, learn how to transform continuous data in a series to binary values by using scikit-learn's Binarizer. FREE ACCESS
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    4.  The MinMaxScaler
    8m 38s
    In this video, you will learn how to apply the MinMaxScaler on a dataset to get two similar columns to have the same range of values. FREE ACCESS
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    5.  The StandardScaler
    7m 54s
    Learn how to standardize multiple columns in your dataset using the StandardScaler from scikit-learn. FREE ACCESS
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    6.  The Normalizer
    8m 54s
    In this video, you will learn how to distinguish between the Normalizer and other scaling techniques, and apply this scaler on the continuous features of a dataset. FREE ACCESS
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    7.  The MaxAbsScaler
    5m 11s
    To find out how to represent the values in a column as a proportion of the maximum absolute value, use the MaxAbsScaler. FREE ACCESS
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    8.  Label Encoding
    8m 46s
    In this video, learn how to apply label encoding to the features and target in your dataset and recognize its limitations when applied to input features. FREE ACCESS
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    9.  One-Hot Encoding
    4m 23s
    During this video, you will learn how to use the Pandas library to one-hot encode one or more features of your dataset and distinguish between this technique and label encoding. FREE ACCESS
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    10.  Exercise: Building ML Training Sets
    7m 31s
    In this video, you will learn how to transform a continuous series into a categorical (binary) one, distinguish between Normalization and other scaling techniques, score each product as a proportion of the top product's sales, and encode the "VehicleType" field which contains values ["Hatchback", "Sedan", "SUV"]. FREE ACCESS

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