Implementing ML Algorithm Using scikit-learn
Python
| 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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6m 8sIn this video, find out how to work with the least absolute shrinkage and selection operator. FREE ACCESS
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7m 15sIn this video, you will learn how to apply Bayesian Ridge regression using the scikit-learn library. FREE ACCESS
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3. Data Classification4m 27sUpon completion of this video, you will be able to describe data classification using scikit-learn. FREE ACCESS
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4. Decision Tree Classification15m 33sDuring 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-learn11m 5sIn 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 Bayes7m 47sIn this video, you will learn how to classify documents using Naive Bayes with scikit-learn. FREE ACCESS
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7. Post Model Validation6m 54sIn 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 Shufflesplit6m 10sIn this video, you will learn how to work with cross model implementation using ShuffleSplit. FREE ACCESS
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9. Brute Force Grid Search3m 23sFind 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 Classifiers4m 33sIn 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
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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