Implementing ML Algorithm Using scikit-learn
Python 3.6.5
| Intermediate
- 10 Videos | 1h 13m 16s
- Includes Assessment
- Earns a Badge
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
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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
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1.Least Absolute Shrinkage6m 8sUP NEXT
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2.Bayesian Ridge Regression Using scikit-learn7m 15s
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3.Data Classification4m 27s
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4.Decision Tree Classification15m 33s
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5.Vector Machine Using scikit-learn11m 5s
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6.Document Classification and Naive Bayes7m 47s
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7.Post Model Validation6m 54s
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8.Using Shufflesplit6m 10s
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9.Brute Force Grid Search3m 23s
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10.Exercise: Working with Decision Tree Classifiers4m 33s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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