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

The square root of the average squared difference between the predicted and actual values.

The proportion of variance in the target variable that is explained by the model.

The variance of the model’s predictions.

What is the range of the area under the ROC curve?

What is the range of the area under the ROC curve?

It ranges from $-1$ to $1$, and a higher score indicates better-defined clusters.

It ranges from $0$ to $1$, and a higher score indicates better-defined clusters.

It ranges from $-1$ to $1$, and a lower score indicates better-defined clusters.

What is the range of the silhouette score, and how do we interpret it?

What is the range of the silhouette score, and how do we interpret it?

Overfitting happens when our model underperforms the baseline error.

We can detect it using regularization.

Overfitting happens when our model adapts well to the data it was trained on but is incapable of generalizing well to new data.

We can detect it using regularization.

Overfitting happens when our model adapts well to the data it was trained on but is incapable of generalizing well to new data.

We can detect it using cross-validation.

What is overfitting, and how can we detect it?

What is overfitting, and how can we detect it?

The leave-one-out (LOO) cross-validation technique

**(Select all that apply.)** Which techniques are good alternatives for the classic train-test-validation split? 

(Select all that apply.) Which techniques are good alternatives for the classic train-test-validation split?

Quiz: Model Evaluation

Quiz: Model Evaluation

Check your understanding of how to evaluate machine learning models.

Course Overview

Introduction to Machine Learning

Preprocessing

Supervised Learning

Unsupervised Learning

Model Evaluation

Tips and Tricks

Conclusion

Quiz: Model Evaluation