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Machine Learning Overview

Machine Learning is a way to let a computer learn patterns from historical data.

In normal programming, we write rules by ourselves:

if user clicks add_to_cart:
    maybe user wants to buy

In machine learning, we prepare examples and let the model learn the pattern:

past user behavior + purchase result -> model learns the pattern

For data engineers, the important idea is:

Machine learning depends on clean, well-organized data.

If the data is messy, duplicated, missing, or designed with the wrong meaning, the model will also be unreliable.

Python Toolset​

For this beginner section, we will use:

  • Python
  • pandas
  • scikit-learn

Install the main packages:

pip install pandas scikit-learn

These tools are enough to learn the basic workflow:

raw data -> pandas DataFrame -> feature table -> scikit-learn model -> evaluation

Example Scenario​

We will use an e-commerce behavior example.

The raw data may contain events like:

  • session_start
  • view_item
  • add_to_cart
  • purchase

The business question is:

Can we predict whether a user will purchase soon?

This is called a purchase propensity problem.

Basic Terms​

Row​

A row is one training example.

For this example, one row should represent one user.

one user = one row

Feature​

A feature is an input column used by the model.

Examples:

  • How many sessions did the user have?
  • How many products did the user view?
  • How many times did the user add items to cart?
  • How many days since the user's last activity?

Label​

A label is the answer we want the model to learn.

For this example:

label = did this user purchase?

The label can be:

  • 1: yes, the user purchased
  • 0: no, the user did not purchase

Model​

A model is the result of training.

After training, the model can receive new user behavior and output a prediction.

Prediction​

A prediction is the model's guess.

For example:

user_001 -> 0.82 probability of purchase
user_002 -> 0.13 probability of purchase

Machine Learning Workflow​

In this basic course, we will focus on:

  • Data preparation
  • Feature engineering
  • Model training
  • Model evaluation
  • Prediction

Common Machine Learning Types​

Classification​

Classification predicts a category.

Examples:

  • Will the user purchase? yes or no
  • Is this transaction fraud? yes or no
  • Is this email spam? spam or not spam

Our purchase prediction example is a classification problem.

Regression​

Regression predicts a number.

Examples:

  • How much revenue will we get tomorrow?
  • How long will delivery take?
  • What will the house price be?

Clustering​

Clustering groups similar data together.

Examples:

  • Group users by behavior
  • Group products by buying pattern
  • Group articles by topic

Why Data Engineers Should Learn This​

Data engineers do not always train models every day, but they often build the data foundation for machine learning.

Common data engineering responsibilities include:

  • Collect raw data from systems
  • Clean and transform data
  • Build reliable feature tables
  • Schedule pipelines
  • Monitor data quality
  • Deliver data to analysts, data scientists, or ML systems

Machine learning projects often fail because the data pipeline is weak, not because the model algorithm is weak.