Page Summary

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• This module focuses on preparing numerical data, such as temperature or weight, for use in machine learning models.

• Machine learning practitioners spend significant time on data preparation tasks like cleaning and transformation.

• The module covers techniques like feature scaling, outlier detection, and binning to improve data quality for model training.

• Learners should have a basic understanding of machine learning concepts before starting this module.

• Categorical data, like postal codes, will be addressed in a separate module due to its distinct characteristics and handling requirements.

• Introduction to Machine Learning

ML practitioners spend far more time evaluating, cleaning, and transformingdata than building models.Data is so important that this course devotes three entire units to the topic:

• Working with numerical data (this unit)
• Working with categorical data
• Datasets, generalization, and overfitting

This unit focuses onnumerical data,meaning integers or floating-point valuesthat behave like numbers. That is, they are additive, countable, ordered,and so on. The next unit focuses oncategorical data, which caninclude numbers that behave like categories. The third unit focuses on how toprepare your data to ensure high-quality results when training and evaluatingyour model.

Examples of numerical data include:

• Temperature
• Weight
• The number of deer wintering in a nature preserve

In contrast, US postal codes, despitebeing five-digit or nine-digit numbers, don't behave like numbers or representmathematical relationships. Postal code 40004 (in Nelson County, Kentucky) isnot twice the quantity of postal code 20002 (in Washington, D.C.). These numbersrepresent categories, specifically geographic areas, and are consideredcategorical data.

• Categorical data
• Numerical data