Implementing ML Algorithm Using scikit-learn

Python    |    Intermediate
  • 10 videos | 1h 13m 16s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 217 users Rating 4.5 of 217 users (217)
Discover how to implement data classification using various techniques, including Bayesian, and learn to apply various search implementations with Python and scikit-learn.

WHAT YOU WILL LEARN

  • Work with least absolute shrinkage and selection operator
    Demonstrate how to apply bayesian ridge regression using scikit-learn
    Describe data classification using scikit-learn
    Implement classifications with decision trees using scikit-learn
    Demonstrate how to work with data classification using vector machines in scikit-learn
  • Demonstrate how to classify documents with naive bayes using scikit-learn
    Work with post model validation using the cross model algorithm
    Demonstrate how to work with cross model implementation using shufflesplit
    Implement poor man's grid search and brute force grid search
    Create labels and features to classify data into train and test datasets and apply decision tree classifiers

IN THIS COURSE

  • 6m 8s
    In this video, find out how to work with the least absolute shrinkage and selection operator. FREE ACCESS
  • 7m 15s
    In this video, you will learn how to apply Bayesian Ridge regression using the scikit-learn library. FREE ACCESS
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    3.  Data Classification
    4m 27s
    Upon completion of this video, you will be able to describe data classification using scikit-learn. FREE ACCESS
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    4.  Decision Tree Classification
    15m 33s
    During this video, you will learn how to implement classifications with decision trees using the scikit-learn library. FREE ACCESS
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    5.  Vector Machine Using scikit-learn
    11m 5s
    In this video, you will learn how to work with data classification using vector machines in scikit-learn. FREE ACCESS
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    6.  Document Classification and Naive Bayes
    7m 47s
    In this video, you will learn how to classify documents using Naive Bayes with scikit-learn. FREE ACCESS
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    7.  Post Model Validation
    6m 54s
    In this video, you will learn how to work with Post model validation using the Cross model algorithm. FREE ACCESS
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    8.  Using Shufflesplit
    6m 10s
    In this video, you will learn how to work with cross model implementation using ShuffleSplit. FREE ACCESS
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    9.  Brute Force Grid Search
    3m 23s
    Find out how to implement a poor man's grid search and a brute force grid search. FREE ACCESS
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    10.  Exercise: Working with Decision Tree Classifiers
    4m 33s
    In this video, you will learn how to create labels and features to classify data into train and test datasets and apply a decision tree classifier. FREE ACCESS

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