Machine learning is the science of designing systems that can learn from data and make data-driven decisions and predictions without the need for explicit programming. ML is considered a subfield of both computer science and AI.
”Predictions require many variables, complicated non linear relationships between them and in some cases are highly stochastic. So it is often only algorithms that can learn those relationships. Humans alone would have hard time.”
– Dr. Danko Nikolic, CDO & Head of AI, Savedroid
This definition speaks volumes about the importance of machine learning. Machine learning, if utilized correctly, can do much more than reduce manual work. ML can, in fact, make it possible to infer solutions and deduce patterns in large sets of data, far beyond the reasoning and deductive capacity of the human brain. It goes without saying then that is has vast potential to make businesses more efficient, intuitive, and profitable. However, the actual application of machine learning to a business depends upon a number of factors.
What Kinds of Problems does ML Solve?
“Any business problem where you have hard data, variability, and a large number of examples.”
– Dr. Ben Waber, CEO, Humanyze
The problem that you want to solve through machine learning should be complex enough that it can NOT be resolved with simple programming using a set of rules.
It is important that the data available for the problem is current and structured. A great way to begin is by applying supervised machine learning to historical data. While applying ML to a field with rapidly changing trends, the data has to be new enough to be relevant to the trends and nuances of the business.
To use supervised machine learning as the starting point, as we recommend, it is important for existing data to be both clean and labeled. For the uninitiated, labels are features that are used to describe the data.
Machine learning is not the right fit for solutions that require great precision, but does great in instances where there is allowance for some margin of error. This makes ML ideal for identifying trends and making predictions based on large amounts of data.
ML Application Strategy
“ML cannot ever become commodity. Success of ML depends strongly on the knowledge, skills and dedication of the people who do it.”
– Dr. Danko Nikolic, CDO & Head of AI, Savedroid
Machine learning works best when all the passion and drive of a critical business problem is used to direct its power. A priority problem whose solution can have a huge impact on the business is a great place to start.
For ML, context is everything. All nuances of a business must be considered while feeding it data. For instance, if it is given all sales data for retail products but no information about promotional deals and asked to predict future demand, you are in trouble. This is because the ML algorithm could end up wrongly predicting demand for items that were bought majorly only as a part of a promotional strategy
ML applications can vary widely based on the problem they are trying to solve. There is no one size-fits-all solution. Furthermore, machine learning requires constant tweaking and adjusting, not to mention data selection and cleaning, as well as live environment testing. It is important to anticipate all this and commit enough resources in advance.
Legitimate Use Cases
“Good CS expert says: Most firms that think they want advanced AI/ML really just need linear regression on cleaned-up data.”
– Dr. Robin Hanson, Professor at George Mason University
E-commerce: Machine learning is being used to process cleaned up historical sales data to make a variety of predictions. It is used to define sales goals, plan campaigns, determine customer lifetime value, churn rate, identify customer issues, and much more.
Fraud detection/security: It is difficult to recognize and respond to cases of frauds or security breaches using fixed algorithms because of the constantly changing and shifting methods used to commit such instances. Machine learning can learn from changing data to detect anomalies and recognize online fraud and/or cyber security issues in real time.
Facial recognition: Facial recognition is another area where it is difficult to process data using a set of rules. Considering that the human brain can identify and label facial features quickly, it is easy to get labeled data for the training sample. This makes it an ideal problem for machine learning.
Share trading: Similar to e-commerce, cleaned up past data can be used to train the system using machine learning. The ML algorithm can then be used to identify trends and make predictions based on real time data.
In a Nutshell
Machine learning has shown remarkable success in solving problems that are beyond the purview of human processing capacity. How does it apply to your business? Once you identify the right type of problem, the next step is to start collecting relevant data and cleaning it. With some effort and persistence, ML has the potential to take your business to the next level. Google goes through trillions of megabytes of data to provide search results within a few seconds while taking into account thousands of variables, something that would be impossible for any number of humans to achieve. THAT is the raw power of machine learning!