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

In which clustering method do we not need to set a number of clusters before running it?

In which clustering method do we not need to set a number of clusters before running it?

Increase the value of the `n_clusters` parameter.

Reduce the value of the `max_iter` parameter.

Change the value of the `random_state` parameter.

In `KMeans`, what can we do if it’s taking too long to run?

In <code>KMeans</code>, what can we do if it’s taking too long to run?

Agglomerative clustering starts with each data point as a separate cluster and then iteratively merges the closest pairs of clusters until all points are in a single cluster.

Divisive clustering starts with all points in a single cluster and then recursively splits it into smaller clusters until each point is in a separate cluster.

Divisive clustering starts with each data point as a separate cluster and then iteratively merges the closest pairs of clusters until all points are in a single cluster.

**(Select all that apply.)** Which of the sentences below regarding hierarchical clustering are correct?

(Select all that apply.) Which of the sentences below regarding hierarchical clustering are correct?

A small value of `eps` (ε) may lead to many small clusters, while a large value may merge different clusters.

A small value for `min_samples` may lead to noise points being included in a cluster, while a large one may result in some clusters not being identified.

A high value for `min_samples` may lead to many small clusters, while a large value may merge different clusters.

**(Select all that apply.)** Which statements regarding DBSCAN are true?

(Select all that apply.) Which statements regarding DBSCAN are true?

Which method is capable of identifying not only clusters but also observations that do not belong to any cluster?

Which method is capable of identifying not only clusters but also observations that do not belong to any cluster?

Quiz: Unsupervised Learning

Quiz: Unsupervised Learning

Check your understanding of unsupervised learning algorithms and how to use them.

Course Overview

Introduction to Machine Learning

Preprocessing

Supervised Learning

Unsupervised Learning

Model Evaluation

Tips and Tricks

Conclusion

Quiz: Unsupervised Learning