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, each machine learning algorithm uses the following series of steps:
- 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, such as a text file, spreadsheet, or database.
- 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. Data preparation involves fixing or cleaning so-called “messy” data, eliminating unnecessary data, and re-coding the data to conform to the learner’s expected inputs.
- Model training: By the time the data has been prepared...
