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Scikit-Learn for Machine Learning

The Machine Learning Process

Explore the essential machine learning process that includes collecting and preparing data, building models using supervised or unsupervised learning, and evaluating model performance. Understand how iterative refinement improves models to solve real-world problems effectively with scikit-learn.

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There is a workflow that we can follow in most ML projects. This workflow consists of four major steps that each feature smaller steps:

  1. Data collection

  2. Preprocessing

  3. Modeling

  4. Model evaluation

Although these steps give us the impression of linearity, the process is usually quite iterative. This means we’re constantly going back and forth between steps.

A basic representation of the ML workflow
A basic representation of the ML workflow

Let’s look at these steps in more detail.

Data collection

Data collection, the initial stage of the ML process, is where raw data is gathered from various sources. This data can come in different forms, including structured and unstructured data, and it may be stored in databases, spreadsheets, text documents, or other formats. The quality and ...