Ever wonder how apps, websites, and machines seem to be able to predict the future? Like how Amazon knows what your next purchase may be, or how self-driving cars can safely navigate a complex traffic situation?
The answer lies in machine learning.
Machine learning is a branch of artificial intelligence (AI) that often leverages Python to build systems that can learn from and make decisions based on data. Instead of explicitly programming the machine to solve the problem, we show it how it was solved in the past and the machine learns the key steps that are required to do the same task on its own.
Machine learning is revolutionizing every industry by bringing greater value to companies’ years of saved data. Leveraging machine learning enables organizations to make more precise decisions instead of following intuition.
There’s an explosive amount of innovation around machine learning that’s being used within organizations, especially given that the technology is still in its early days. Many companies have invested heavily in building recommendation and personalization engines for their customers. But, machine learning is also being applied in a huge variety of back-office use cases as well, like to forecast sales, identify production bottlenecks, build efficient traffic routing systems, and more.
Machine learning algorithms fall into two categories: supervised and unsupervised learning.
Supervised learning tries to predict a future value by relying on training from past data. For instance, Netflix’s movie-recommendation engine is most likely supervised. It uses a user’s past movie ratings to train the model, then predicts what their rating would likely be for movies they haven’t seen and recommends the ones that score highly.
Supervised learning enjoys more commercial success than unsupervised learning. Some common use cases include fraud detection, image recognition, credit scoring, product recommendation, and malfunction prediction.
Unsupervised learning is about uncovering hidden structures within data sets. It’s helpful in identifying segments or groups, especially when there is no prior information available about them. These algorithms are commonly used in market segmentation. They enable marketers to identify target segments in order to maximize revenue, create anomaly detection systems to identify suspicious user behavior, and more.
For instance, Netflix may know how many customers it has, but wants to understand what kind of groupings they fall into in order to offer services targeted to them. The streaming service may have 50 or more different customer types, aka, segments, but its data team doesn’t know this yet. If the company knows that most of its customers are in the “families with children” segment, it can invest in building specific programs to meet those customer needs. But, without that information, Netflix’s data experts can’t create a supervised machine learning system.
So, they build an unsupervised machine learning algorithm instead, which identifies and extracts various customer segments within the data and allows them to identify groups such as “families with children” or “working professionals.”