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

When we use `MinMaxScaler` to scale a feature, what will be its range?

When we use <code>MinMaxScaler</code> to scale a feature, what will be its range?

When we use `StandardScaler` to scale a feature, what will be its range?

When we use <code>StandardScaler</code> to scale a feature, what will be its range?

Mean = $0$ \
Standard deviation = same as the original feature

Mean = same as the original feature \
Standard deviation = $1$

When we use `StandardScaler` to scale a feature, what will be its mean and standard deviation?

When we use <code>StandardScaler</code> to scale a feature, what will be its mean and standard deviation?

If we want to discretize a continuous variable in a way that each class has the same number of observations, which class should we use?

If we want to discretize a continuous variable in a way that each class has the same number of observations, which class should we use?

Imagine we have a categorical column that we want to turn numerical, while keeping it in one column. Which encoder should we use?

Imagine we have a categorical column that we want to turn numerical, while keeping it in one column. Which encoder should we use?

If our original dataset has 10 features and we apply PCA to it in the following way:
```python
# Perform PCA
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
```
How many columns would we get at the end?

If our original dataset has 10 features and we apply PCA to it in the following way:
<pre><code class="language-python"># Perform PCA
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
</code></pre>
How many columns would we get at the end?

Quiz: Preprocessing

Quiz: Preprocessing

Check your understanding of preprocessing and its steps.

Course Overview

Introduction to Machine Learning

Preprocessing

Supervised Learning

Unsupervised Learning

Model Evaluation

How to Predict the Traffic Volume Using Machine Learning

Tips and Tricks

Conclusion

Customer Segmentation with K-Means Clustering

Quiz: Preprocessing