Summary
In this chapter, you were introduced to the fundamental concepts behind machines that can learn from data. After stripping away the futuristic gloss of AI, we went through a series of practical business scenarios where we saw intelligent algorithms at work. These examples showed us how, if we look carefully, we can often recognize occasions to leverage machines for getting intellectual work done. We saw that, as an alternative to the traditional mode of operating, there is an ML way to get things done: whether we are predicting prices, segmenting consumers, or optimizing a digital advertising strategy, learning algorithms can be our tireless companions. If we coach them well, they can extend human intelligence and provide a sound competitive advantage to our business. We explored the differences among the three types of learning algorithms (supervised, unsupervised, and reinforcement) and understood the fundamental drivers that can guide us in selecting which algorithms to...