In part 3, you learned about the various considerations that come into play when trying to make the optimal forward-looking predictions based on data: velocity, variety, volume and quality. All of these factors need to be considered when analyzing data sources, compiling learnings and leveraging them to make future predictions.
Learning lies at the heart of the data science workflow. Machine learning gives systems the ability to learn from data to improve the performance of specific tasks, without being explicitly programmed to do so. For example, in industry, machine learning is used to detect patterns in the health of heavy machinery, equipment and components in order to predict when they will break down or require maintenance.
Imagine you are a fleet manager responsible for a number of delivery trucks. Machine learning algorithms can help you predict whether a delivery will be successful, in real time. In part four (episode one), Manny B. walks us through this example:
