In the business world, data is king. The more data a company has, the better equipped it is to make informed decisions about its products, services, and customers. However, navigating through all of that data can be a daunting task. That's where machine learning comes in. Machine learning is a subset of artificial intelligence that allows computers to learn from data without being explicitly programmed.
As per a report by McKinsey, the adoption of AI is still increasing steadily and 56% of all respondents in a poll, claim adoption in at least one function.
Techniques for Machine Learning
Business applications of machine learning are becoming more widespread and commonplace. Here are four essential machine learning techniques that businesses should be aware of and understand how to utilize.
Supervised Learning
Supervised learning is where the machine learning algorithm is given a set of training data that has been labeled with the desired output. The algorithm then ‘learns’ the mapping from input to output and can be used to make predictions on new, unseen data.
One common example of supervised learning is spam filtering. A supervised learning algorithm is ‘trained’ on a set of emails that have been labeled as spam or not spam. The algorithm then learns to identify features that are indicative of spam emails which can then be used to filter new emails as they come in, automatically classifying them as spam or not spam.
Unsupervised Learning
Unsupervised machine learning is a type of machine learning where the data is not labeled, and the algorithms are left to find structure in the data on their own. Engineers do this by clustering data points together or by reducing the dimensionality of the data. Unsupervised machine learning is often used for exploratory data analysis to find hidden patterns in the data.
For example, an unsupervised learning algorithm could be used to group customers together based on their purchase history. The algorithm would find patterns in the data and group customers who have similar purchase patterns.
Semi-Supervised Learning
Semi-supervised learning is a combination of supervised and unsupervised learning. The machine is exposed to both labeled and unlabeled data, and the goal is to learn from both. It is useful when there is a lot of data, but not all of it is labeled, or when the labels are not completely accurate.
A common example of semi-supervised learning is document categorization. It is when a machine learning algorithm is given a set of documents that have been labeled with their category (e.g. sports, politics, business), but there are also a number of unlabeled documents. The algorithm has to learn the structure of the documents and also predict the category of the unlabeled documents.
Reinforcement Learning
Reinforcement learning is where the machine learning algorithm is given a set of data and a goal but is not told how to achieve the goal. The algorithm has to learn by trial and error what actions will lead to the goal being achieved.
One common example of reinforcement learning is playing a game. The machine learning algorithm is given the rules of the game, and a goal (e.g., win the game) but has to figure out by itself what actions to take in order to achieve the goal.
Which Machine Learning Method is best for Business Applications?
There is no simple answer to the question of which machine learning method is best for business applications. It depends on the specific business application and the data available. Some machine learning methods are more suitable and efficient for certain types of applications than others.
Ultimately, the best machine learning method for a business application is the one that produces the best results for that application. Businesses should experiment with different methods and compare the results to find the one that perfectly matches their needs.
Conclusion
According to statistics on artificial intelligence from a 2019 survey by NewVantage, more than nine out of ten (90%) of the top corporations polled say they continue to invest in AI. It is a powerful tool that can be used to improve business applications. However, as an organization, you have various options when it comes to training algorithms for your programs, and the best one will depend on the specific requirement.