We are living in the era of technological transformation that is bringing about changes in the way we take decisions. As big data is becoming pervasive across all the industries, use of machines to find patterns and predict future is gaining a lot of prominence in the market.
Machine learning is a method of data analysis which automates the process of model building. The algorithms use computational techniques to generate insights that help organizations make better decisions. Machine learning is used in different areas in real-time business situations. Here are a few widely used examples of machine learning applications you must be familiar with:
How Machine Learning Works?
Machine learning uses three types of techniques. These techniques train a model and predict outputs based on the following:
Supervised Machine Learning: It consists of input variables (x) and an output variable (Y). A supervised algorithm then uses the training data to map inputs to the desired output. It is called supervised learning because it requires human interference in making predictions on the training data. The algorithm makes a number of iterations to get the acceptable level of output. Supervised machine learning is used in classification and regression problems. Some of the common applications of this technique include:
Unsupervised Machine Learning: In unsupervised machine learning there is no outcome variable. The algorithm models the data using input variables and presents the structure based on the same. Unsupervised machine learning is used in forming classification (clusters) and associations in data. Examples of this technique include:
Reinforcement Learning: In this technique, the machine produces programs, called agents, through a process of learning and evolving. The agent learns from past consequences of its actions and selects the best possible solution through trial and error learning. Applications of this technique include Hidden Markov models.
When to Get Started with Machine Learning?
Machine learning aids in solving business problems involving a large amount of data. In order to use machine learning, organizations need to have scalable data preparation capabilities. The machine learning algorithms quickly produce models that can analyze complex data, and deliver faster and accurate insights.
With these models, data analysts/scientists can identify profitable opportunities and mitigate potential risks. However, organizations should first need to choose the right technique and algorithm to make the best use of machine learning.
Machine Learning by Industries
Machine learning is proving its worth in many industries globally. It significantly drives efficiency; deliver customer value and helps in gaining actionable insights. Some of the key sectors that embrace machine learning religiously include:
Financial Services: The financial services industry was one of the first sectors to implement artificial intelligence in business decision-making. Fraud detection, face recognition, compliance are carried out meticulously through machine learning with a large amount of structured and unstructured data.
Retail: Machine learning helps retailers to increase sales and customer engagement through predictive analytics such as market basket analysis, item recommendations, analyzing buyer sentiment, ad scoring, and identifying new markets, among others.
Healthcare: Machine learning offers an array of benefits to patients and healthcare providers. It is used in discovering a correlation between patient behavior and disease. Use of biometric sensors is saving lives of millions of patients globally. Machine learning is extremely crucial in clinical trials as it helps to know if the treatment would be safe and effective.
Machine learning heralds significant potential for the growth of humans and the economy. According to a market research firm, the machine learning as a service market (MLAAS) is estimated to grow from US$613.4m in 2016 to US$3,755m by 2021, at a CAGR of 43.7%. Machine learning will radically transform processes and make our lives and businesses efficient. It will reduce the need for human interventions and has fascinating implications for the global industries. So, are we ready for it?