Machine Learning is the name commonly applied to a number of Bayesian techniques used for pattern recognition and learning. At its core, machine learning is a collection of algorithms that can learn from and make predictions based on recorded data, optimize a given utility function under uncertainty, extract hidden structures from data and classify data into concise descriptions. Machine Learning is often deployed where explicit programing is too rigid or is impractical. Unlike regular computer code that is developed by software developers to try to generate a program code-specific output based on given input, machine learning uses data to generate statistical code (an ML model), that will output the “right result” based on a pattern recognized from previous examples of input (and output, in the case of supervised techniques). The accuracy of an ML model is based mainly on the quality and quantity of the historical data.
With the right data, an ML model can analyze high dimensional problems with billions of examples, to find the optimal function that can predict an outcome with a given input. ML models can usually provide statistical confidence on predictions, as well as on its overall performance. Such evaluation scores are important in the decision if you are to use an ML model or any individual prediction.
Implementing Machine Learning in your Business
Machine learning is often used to predict future outcomes based on historical data. For example, organizations use machine learning to predict how many of their products will be sold in future fiscal quarters based on a particular demographic; or estimate which customer profile has the highest probability to become dissatisfied or the most loyal to your brand. Such predictions allow better business decisions, more personal user experience, and the potential to reduce customer retention costs. Complementary to business Intelligence (BI), which focuses on reporting past business data, ML predicts future outcomes based on past trends and transactions.
There are several steps that comprise a successful implementation of ML in a business. First, identifying the right problem — identifying the prediction that would benefit the business if ascertained. Next, the data must be collected, based on historical business metrics (transactions, sales, attrition, etc.). Once the data is aggregated, an ML model can be built based on that data. The ML model is run and the prediction output of the model is applied back to the business system to make more informed decisions.