Summary  
This chapter introduces supervised machine learning, where a model is trained on labeled examples to learn distinguishing patterns and then evaluated on a separate test set to assess its performance on unseen data.  

General domain of usage  
Email spam detection.

To follow the code in this course, it is useful to complete the following courses first, unless these topics are already familiar:

**Machine learning (ML)** is an approach to programming where computers learn from data to solve a task instead of being given explicit instructions.

Consider the example of a **spam**/**ham** (not spam) classifier.


Building such a system with a traditional programming approach (without ML) is difficult, as it requires writing explicit rules, even manually compiling a list of spam words.

With machine learning, the model is trained on many examples of spam and ham emails and learns the distinguishing patterns on its own.



The data provided for training is called the **training set**. In this case, it consists of emails already **labeled** as spam or ham, which enables the model to capture the characteristics of both categories.

After training, the model is evaluated using a **test set** - a separate collection of labeled emails. This step checks how well the model generalizes to new, **unseen data**.


What is the primary advantage of using a machine learning (ML) model for a spam/ham classifier compared to a traditional programming approach?

Master the fundamentals of Machine Learning and the Scikit-learn library. Explore the complete ML workflow, from handling missing values and encoding categorical data to scaling features. Build efficient, leak-proof data preprocessing pipelines using ColumnTransformer. Transform raw datasets into model-ready structures and implement robust predictive pipelines.

What is ML