Implementing ML Algorithm Using scikit-learn

Python 3.6.5    |    Intermediate
  • 10 Videos | 1h 17m 46s
  • Includes Assessment
  • Earns a Badge
Likes 38 Likes 38
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

  • Playable
    1. 
    Least Absolute Shrinkage
    6m 8s
    UP NEXT
  • Playable
    2. 
    Bayesian Ridge Regression Using scikit-learn
    7m 15s
  • Locked
    3. 
    Data Classification
    4m 27s
  • Locked
    4. 
    Decision Tree Classification
    15m 33s
  • Locked
    5. 
    Vector Machine Using scikit-learn
    11m 5s
  • Locked
    6. 
    Document Classification and Naive Bayes
    7m 47s
  • Locked
    7. 
    Post Model Validation
    6m 54s
  • Locked
    8. 
    Using Shufflesplit
    6m 10s
  • Locked
    9. 
    Brute Force Grid Search
    3m 23s
  • Locked
    10. 
    Exercise: Working with Decision Tree Classifiers
    4m 33s

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