Learn how to build and evaluate machine learning models using scikit-learn, from data preprocessing to model selection and evaluation.

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Mlxtend

This comprehensive course is designed to develop the knowledge and skills to effectively utilize the scikit-learn library in Python for machine learning tasks. It is an excellent resource to help you develop practical machine learning applications using Python and scikit-learn.

In this course, you’ll learn fundamental concepts such as supervised and unsupervised learning, data preprocessing, and model evaluation. You’ll also learn how to implement popular machine learning algorithms, including regression, classification, and clustering, using scikit-learn’s user-friendly API. The course also introduces advanced topics such as ensemble methods, model interpretation, and hyperparameter optimization.

After taking this course, you’ll gain hands-on experience in applying machine learning techniques to solve diverse data-driven problems. You’ll also be equipped with the expertise to confidently leverage scikit-learn for a wide range of machine learning applications in industry as well as academia.

Scikit-Learn for Machine Learning

```python
pipeline = Pipeline([
    MinMaxScaler(),
    PCA(),
    LinearRegression()
])
```

```python
pipeline = Pipeline([
    ('scaler', MinMaxScaler()),
    ('pca', PCA()),
    ('regressor', LinearRegression())
])
```

How can we build a pipeline containing a scaler, a dimensionality reduction, and a regressor?

How can we build a pipeline containing a scaler, a dimensionality reduction, and a regressor?

Because they’re easier to use than other models

Because they improve the performance of our models

Because they provide a benchmark against which we can compare our more complex models

Why is it important to use baselines, such as `DummyClassifier` and `DummyRegressor`?

Why is it important to use baselines, such as <code>DummyClassifier</code> and <code>DummyRegressor</code>?

For each feature, calculate the model’s accuracy and divide it by the number of features.

Calculate the decrease in performance caused by permuting the feature.

Use the `.feature_importance()` method that comes in scikit-learn.

How can we estimate feature importance using scikit-learn?

How can we estimate feature importance using scikit-learn?

**(Select all that apply.)** What are some reasons for calculating feature importance? 

(Select all that apply.) What are some reasons for calculating feature importance?

What are the commands to export and import a model using joblib?

What are the commands to export and import a model using joblib?

Quiz: Tips and Tricks

Quiz: Tips and Tricks

Check your understanding of pipelines, baselines, feature importance, and model persistence.

Course Overview

Introduction to Machine Learning

Preprocessing

Supervised Learning

Unsupervised Learning

Model Evaluation

Tips and Tricks

Conclusion

Quiz: Tips and Tricks