- Google Cloud
- 2 weeks
- 5-7 hours a week
- $50/month (7-day Free Trial)
- Course Certificate
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Want to know how you can improve the accuracy of your machine learning models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering on Google Cloud Platform where we will discuss the elements of good vs bad features and how you can preprocess and transform them for optimal use in your machine learning models.
In this course you will get hands-on practice choosing features and preprocessing them inside of Google Cloud Platform with interactive labs. Our instructors will walk you through the code solutions which will also be made public for your reference as you work on your own future data science projects.
Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering where we will discuss good vs bad features and how you can preprocess and transform them for optimal use in your models.
Raw Data to Features
Feature engineering is often the longest and most difficult phase of building your ML project. In the feature engineering process, you start with your raw data and use your own domain knowledge to create features that will make your machine learning algorithms work. In this module we explore what makes a good feature and how to represent them in your ML model.
Preprocessing and Feature Creation
This section of the module covers pre-processing and feature creation which are data processing techniques that can help you prepare a feature set for a machine learning system.
In traditional machine learning, feature crosses don’t play much of a role, but in modern day ML methods, feature crosses are an invaluable part of your toolkit.In this module, you will learn how to recognize the kinds of problems where feature crosses are a powerful way to help machines learn.
TensorFlow Transform (tf.Transform) is a library for preprocessing data with TensorFlow. tf.Transform is useful for preprocessing that requires a full pass the data, such as: - normalizing an input value by mean and stdev - integerizing a vocabulary by looking at all input examples for values - bucketizing inputs based on the observed data distribution In this module we will explore use cases for tf.Transform.
Here we recap the major points you learned in each module on Feature Engineering: Selecting Good Features, Preprocessing at Scale, Using Feature Crosses, and Practicing with TensorFlow.
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