Real-World Machine Learning

Chapter 2. Real-world data

This chapter covers

• Getting started with machine learning
• Collecting training data
• Using data-visualization techniques
• Preparing your data for ML

In supervised machine learning, you use data to teach automated systems how to make accurate decisions. ML algorithms are designed to discover patterns and associations in historical training data; they learn from that data and encode that learning into a model to accurately predict a data attribute of importance for new data. Training data, therefore, is fundamental in the pursuit of machine learning. With high-quality data, subtle nuances and correlations can be accurately captured and high-fidelity predictive systems can be built. But if training data is of poor ...