• Introduction to Course
    • 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.
  • 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.
  • Feature Crosses
    • 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
    • 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.
  • Summary

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