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

A subfield of artificial intelligence (AI) that focuses on developing algorithms that can learn from data and make predictions without  explicit programming

A system for automatically generating new ideas or concepts based on data inputs without the need for human creativity or innovation

A type of software program that automatically learns and adapts based on user input, allowing it to perform complex tasks without human intervention

Supervised learning requires more data than unsupervised learning.

In supervised learning, the algorithm learns from labeled data, while in unsupervised learning, the algorithm learns from unlabeled data.

Supervised learning is used for classification tasks, while unsupervised learning is used for clustering tasks.

What is the difference between supervised and unsupervised learning?

What is the difference between supervised and unsupervised learning?

Data collection, feature engineering, model training, and evaluation

Preprocessing, optimization, visualization, and model training

Data collection, preprocessing, modeling, and evaluation

What are the four major steps (in order) of the ML workflow?

What are the four major steps (in order) of the ML workflow?

Importing data from external sources such as CSV files

Telepathically transferring data from another dimension

**(Select all that apply.)** Which of the options below are valid ways to import data to use with scikit-learn?

(Select all that apply.) Which of the options below are valid ways to import data to use with scikit-learn?

```
from sklearn.datasets import make_regression

X, y = make_regression(
       n_samples=100,
       n_features=10,
       noise=0.5
)
```

```
from sklearn.datasets import regression

X, y = regression(
   n_samples=100,
   n_features=10,
   noise=0.5
)
```

```
from sklearn.datasets import make_blobs

X, y = make_blobs(
   n_samples=100,
   centers=4
)
```

Which of the options below allows us to create synthetic data for a regression task?

Which of the options below allows us to create synthetic data for a regression task?

Quiz: Introduction to Machine Learning

Quiz: Introduction to Machine Learning

Test your understanding of the ML process and importing data with scikit-learn.

Course Overview

Introduction to Machine Learning

Preprocessing

Supervised Learning

Unsupervised Learning

Model Evaluation

Tips and Tricks

Conclusion

Quiz: Introduction to Machine Learning