Machine learning in practice

So far, we've focused on how machine learning works in theory. To apply the learning process to real-world tasks, we'll use a five-step process. Regardless of the task at hand, any machine learning algorithm can be deployed by following these steps:

  1. Data collection: The data collection step involves gathering the learning material an algorithm will use to generate actionable knowledge. In most cases, the data will need to be combined into a single source like a text file, spreadsheet, or database.
  2. Data exploration and preparation: The quality of any machine learning project is based largely on the quality of its input data. Thus, it is important to learn more about the data and its nuances during a practice called data ...

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