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 operatordemonstrate how to apply Bayesian Ridge regression using scikit-learndescribe data classification using scikit-learnimplement classifications with decision trees using scikit-learndemonstrate how to work with data classification using vector machines in scikit-learn
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demonstrate how to classify documents with Naive Bayes using scikit-learnwork with Post model validation using the Cross model algorithmdemonstrate how to work with cross model implementation using Shufflesplitimplement poor man's grid search and brute force grid searchcreate 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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COURSE
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