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

Regularization reduces the complexity of a model to avoid overfitting.

Regularization reduces the complexity of a model to avoid underfitting.

Regularization increases the complexity of a model so that it captures all the nuances in the data.

By using the `LinearRegression` and `Lasso` classes

How can we do regularization in linear regression?

How can we do regularization in linear regression?

The higher the `alpha` hyperparameter, the stronger the regularization and the higher the risk of overfitting.

The higher the `alpha` hyperparameter, the weaker the regularization and the lower the risk of overfitting.

The higher the `alpha` hyperparameter, the stronger the regularization and the lower the risk of overfitting.

In the `SGDClassifier` and `SGDRegressor` models, how does the hyperparameter `alpha` impact the mode?

In the <code>SGDClassifier</code> and <code>SGDRegressor</code> models, how does the hyperparameter <code>alpha</code> impact the mode?

By setting `weights='uniform'` in the `KNeighborsRegressor` instance

By setting `k='distance'` in the `KNeighborsRegressor` instance

By setting `weights='distance'` in the `KNeighborsRegressor` instance

In the `KNeighborsRegressor` algorithm, how can we ensure that the closer neighbors will have a higher importance?

In the <code>KNeighborsRegressor</code> algorithm, how can we ensure that the closer neighbors will have a higher importance?

The higher the `max_depth`, the more complex the tree and the higher the risk of overfitting.

The lower the `max_features`, the lower the risk of overfitting.

The lower the `min_samples_split`, the less likely the tree is to split nodes with a small number of samples and the lower the risk of overfitting.

**(Select all that apply.)** Which statement(s) about decision trees is(are) true?

(Select all that apply.) Which statement(s) about decision trees is(are) true?

Different types (e.g., tree-based and deep learning models) and of similar predictive power

Different types (e.g., tree-based and deep learning models) and of different predictive power.

Same types (e.g., Ridge and Lasso regression models) and of similar predictive power.

What mix of models will make an effective ensemble based on averaging?

What mix of models will make an effective ensemble based on averaging?

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

Course Overview

Introduction to Machine Learning

Preprocessing

Supervised Learning

Unsupervised Learning

Model Evaluation

Tips and Tricks

Conclusion

Quiz: Supervised Learning