Machine learning
As your data grows in size, it opens a lot of opportunities for businesses to go beyond understanding business trends and patterns. Machine learning and artificial intelligence are examples of innovations that you can exploit with your data. Building your artificial intelligence and ML capabilities is relatively easy now because of the availability of the requisite technologies and the ability to scale your storage and compute on the cloud.
Machine learning and artificial intelligence are terms that are often mixed up. In a nutshell, machine learning is a subset (or application) of artificial intelligence. Machine learning aims to allow systems to learn from past datasets and adapt automatically without human assistance. This is made possible by a series of algorithms being applied to the dataset; the algorithm analyzes the data in near-real-time and then comes up with possible actions based on accuracy or confidence derived from previous experience.
The word "learning" indicates that the program is constantly learning from data fed to it. The aim of machine learning is to strive for accuracy rather than success. There are three main categories of machine learning algorithms: supervised, unsupervised, and reinforcement.
Supervised machine learning algorithms create a mapping function to map input variables with an output variable. The algorithm uses existing datasets to train itself to predict the output. Classification is a form of supervised ML that can be used in applications such as image categorization or customer segmentation, which is used for targeted marketing campaigns.
Unsupervised machine learning, on the other hand, is when you let a program find a pattern of its own without any labels. A good example is understanding customer purchase patterns when buying products. You get inherent groupings (clustering) according to purchasing behaviors, and the program can associate customers and products according to patterns of purchase. For instance, you may discern that customers who buy Product A tend to buy Product B too. This is an example of a user-based recommendation algorithm and market-based analysis. What it would eventually mean for users is that when they buy a particular item, such as a book, the user is also encouraged to buy other books that belong to the same series, genre, or category.
Reinforcement Learning (RL) provides meaningful insights and actions based on rewards and punishment. The main difference between this and supervised learning is that it does not need labeled input and output as part of the algorithm. An excellent example of this is the new financial trend for "robo-advisors." Robo-advisors run using agents that get rewarded and punished based on their stock performance (that is, gains and losses). In time, the agent can recognize whether to hold, buy, or sell stocks. This has been a game-changer because, in the past, analysts had to make every single decision; now most of the complicated data trends are already analyzed for you and analysts can choose to listen to the agent or not. However, financial trading is very complex given the nature of parameters present in the world, and so not all robo-advisors' predictions are accurate.
