Welcome! This lesson paves your path toward understanding machine learning and the powerful Python library, sklearn. Machine learning, an application of artificial intelligence, enables systems to learn and improve without being explicitly programmed. It plays a key role in various sectors, such as autonomous vehicles, voice recognition systems, and recommendation engines.

Suppose you aim to predict housing prices as an illustration. This scenario constitutes a standard supervised learning problem wherein you train your model using past data. With sklearn, you can import the data, preprocess it, select an algorithm (like linear regression), train the model with the training data, and make predictions. All these steps can be accomplished without manually implementing algorithms.

Datasets form the backbone of machine learning. In this course, we'll use the Iris dataset, which consists of measurements — namely, sepal length, sepal width, petal length, and petal width — for 150 flowers representing three species of iris.

Sklearn provides an easy-to-use load_iris function to import the Iris dataset. Let's see how it works:

Here, the load_iris() function loads the dataset and assigns it to the iris variable. We then separate the dataset into X for features and for the target.