What is Machine Learning? How can it help your business?
Gone are the days when enterprises would have relied solely on human intelligence for their strategic growth and business operations. Today, as machines are becoming smarter than ever, machine learning is influencing enterprises heavily and changing the face of businesses like never before. Enterprises are leveraging the power of data and smart computer systems to learn independently and helping drive innovation in business. If you want to know how machine learning can help your enterprise, then read on.
What is Machine Learning?
Machine learning is a way of analyzing data by computers for automating the building of analytical models. Various algorithms help the machines to learn from data iteratively. This helps computers to discover insights and information hidden in the data. Kindly note that the machines are not explicitly programmed where to look.
The iterative nature of the entire process helps the machines to be exposed to new data and get further information, thus forming a credible insight. Arthur Samuel first defined ‘machine learning’ in 1959 as the “the ability to learn without being explicitly programmed.” With machine learning, a machine can learn from its own mistakes and reprogram itself to improve its performance over time.
Why is Machine Learning important?
Machine learning helps to extract valuable and meaningful insights out of raw data. This helps to solve complex business problems that are data-rich, but which cannot be resolved by human judgement or explicit software engineering or any other traditional approach.
Recent advances in machine learning has enabled systems to rapidly produce analytical models for bigger volumes of highly complex data. The quick delivery of accurate results help in deriving high-value predictions that can drive effective decision making without any human intervention. Humans normally can produce 2-3 good models per week, but machine learning can help create thousands of them per week.
How does Machine Learning happen?
There are 4 famous machine learning methods. Supervised learning accounts for 70% of the machine learning. Unsupervised learning accounts for 10-20%. Reinforcement learning and semi-supervised learning are two other famous methodologies that are used frequently. Let’s look into each one of them in detail to know them better.
Supervised learning algorithms are the ones that are trained using labelled examples. The example can be an input when the output is known. A set of inputs is provided to the learning algorithm, and the corresponding outputs are also provided. The algorithms then learn by comparing the correct output with actual outputs. The errors help the machine to learn and modify the model accordingly. Classification, regression, gradient boosting and prediction help in this process. It is mainly used for predicting future events based on historical data.
This is used when we don’t have historical labels. The correct answers are not informed to the system. The machine is supposed to explore the data and find a structure inside it. K-means clustering, self-organizing maps, singular value decomposition and nearest neighbor mapping are techniques that help in this process.
Both labeled and unlabeled data are used for this learning technique. Labeled data is lesser in amount and methods like regression, classification and prediction are used. It is used when cost of labelling is too high.
Used for gaming, robotics and navigation, this technique is used for machines to learn from trial and error, which actions will lead to best results. The decision maker, the environment and the actions make up the three components.
How can it help your enterprise?
To know how machine learning can help your enterprise, you need to understand the various applications of machine learning technologies. The following are the areas where the predictive powers of machine learning can prove to be a boon.
Insights into processes and customers
Enterprises usually use analytical reporting tools and digital dashboards to gain insights into the customer behavior. There are many BI tools that use simple predictive models for future projections and causative trends. The customer behavior and efficiency of processes that is conveyed by this technique helps to improve business decisions and optimize processes. These lead to better customer experience.
Intelligent customer interactions
Today, enterprises are investing heavily on predictive models to improve the business processes. Your enterprise can detect a possible fraud during a point of sale (PoS). This can lead the system to adjust the digital content and mitigate risk by proactively initiating required steps.
Customer engagement redefined
Looking into the raw data, you will be able to figure out the applications and features in your product that are most often used by customers. You will get to know the features that add most value to your customers and hence, will be able to initiate improvement process for those that are lagging behind.
There are various industries that are leveraging the power of machine learning. Let’s have a quick look.
Machine learning is helping in warranty reserve estimation, demand forecasting, process optimization, predictive maintenance and telematics.
In retail, machine learning has enabled predictive inventory planning, upsell and cross-channel marketing, market segmentation, recommendation engines and customer ROI delivery.
Risk analytics and regulation can be greatly improved through machine learning. Cross-selling and up-selling are again made possible by machine learning. You can also do evaluation of credit-worthiness of a customer and risk analytics. Marketing campaign management has also become easier and effective through machine learning techniques.
Machine learning helps in diagnostics of real-time data of patients. Disease identification and risk stratification is also made possible. Proactive health management and patient triage optimization is also made possible.
How does the future look!
Machine learning is helping enterprises to re-engineer their business processes. Processes in sales, logistics, marketing and even procurement are getting revamped. Since the algorithms are iterative in nature, they are getting better every day, and the process of repeated learning and probing is helping optimize the results. Today, humans can be free of coding everything explicitly and can leave the task of regular improvement to the machines. With calculations happening in milliseconds and optimization happening every hour, decision-making by machines is helping enterprises scale up progressively.
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