Knowledge

Machine Learning: Why is it important?

Most of us would find it hard to go a full day without using at least one app or web service driven by machine learning. But what is Machine Learning (ML), exactly Though the term machine learning has become increasingly common, many people still don’t know exactly what it means and how it is applied, nor do they understand the role of machine learning algorithms and datasets in data science. Here, we will examine the question “What is ML?” We will provide insight into how data scientists and others use machine learning, how it was developed, and what lies ahead as it continues to evolve.

What is Machine Learning?

Machine Learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed. Machine learning algorithms use historical data as input to predict new output values.

Today, with the rise of big data, the Internet of Things (IoT), and ubiquitous computing, machine learning has become essential for solving problems across numerous areas, such as

  • Computational finance (credit scoring, algorithmic trading)
  • Computer vision (facial recognition, motion tracking, object detection)
  • Computational biology (DNA sequencing, brain tumor detection, drug discovery)
  • Automotive, aerospace, and manufacturing (predictive maintenance)
  • Natural language processing (voice recognition)

machine learning

How does it work?

Machine learning works on different types of algorithms and techniques. These algorithms are created with the help of various ML programming languages. Usually, a training dataset is fed to the algorithm to create a model.

Now, whenever input is provided to the ML algorithm, it returns a result value/prediction based on the model. Now, if the prediction is accurate, it is accepted and the algorithm is deployed. However, if the prediction is not accurate, the algorithm is trained repeatedly with a training dataset to arrive at an accurate prediction/result.

Consider this example:

If you wish to predict the weather patterns in a particular area, you can feed the past weather trends and patterns to the model through the algorithm. This will be the training dataset for the algorithm. Now if the model understands perfectly, the result will be accurate.

Why is Machine Learning important?

Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.

All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. By building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

Benefits of Machine Learning

Machine learning has incredibly wide-ranging benefits across almost every facet of life. These are just some of the universal benefits of machine learning:

Predicting customer behavior

Analyses of consumer purchase patterns help give companies insight into the way forward for product and service lines. These patterns can be as precise as why a customer may opt for one product over another, the influences of pricing, season, brand loyalty, and more on these decisions. Such data-oriented findings are made much faster with machine learning and speed is the key to smarter decision-making.

Sustained accuracy in data entry

The most boring of human tasks is that of data entry. The chances of an error are high with such repetitive tasks. These errors can prove costly to a company on several levels. Machine learning ensures that data entry is completed quickly, and with precision, leaving no room for error. It also takes mundane tasks away from employees allowing them to concentrate on more challenging and business-beneficial jobs.

Discovering leads in user experiences

Every business grows based on new leads that convert to paying customers. Being able to stay at the top of your game is about evolving to meet the needs of the customer. Machine learning helps businesses by diving into customer journeys providing insights into trends and anticipating needs. Research has shown that machine learning has made a difference in the upward growth trajectory of businesses by helping them to predict customer behaviors, find inefficiencies, etc.

machine learning

Maintaining a competitive edge

Businesses can grow alongside the market when they have good business intelligence to fall back on. Machine learning has an important role to play here in providing businesses with insights into their unique selling points and their positive aspects in comparison to competing brands. Any new approach can be quickly hypothesized and tested based on available data and help businesses build a go-to-market plan quickly.

Powering virtual assistants

Workplaces, big or small, are about increasing efficiency and making smart use of worker hours. Machine learning, when applied to automatic speech training, helps create smarter and more efficient virtual assistants, who can take down notes, develop minutes of meetings, and maintain better records. All this reduces mundane paperwork that is essential but tiring to do. With better virtual assistants, precision is ensured and privacy regulations are well met.

Types of Machine Learning

  • Supervised learning: In this type of machine learning, data scientists supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm are specified.
  • Unsupervised learning: This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. The data that algorithms train on as well as the predictions or recommendations they output are predetermined.
  • Semi-supervised learning: This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its understanding of the data set.
  • Reinforcement learning: Data scientists typically use reinforcement learning to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.

The Future of Machine Learning: Hybrid AI

For all of its shortcomings, machine learning is still critical to the success of AI. This success, however, will be contingent upon another approach to AI that counters its weaknesses, like the “black box” issue that occurs when machines learn unsupervised. That approach is symbolic AI or a rule-based methodology for processing data. A symbolic approach uses a knowledge graph, which is an open box, to define concepts and semantic relationships.

Together, ML and symbolic AI form hybrid AI, an approach that helps AI understand language, not just data. With more insight into what was learned and why this powerful approach is transforming how data is used across the enterprise.

machine learning

Conclusion

Computers can learn, memorize, and generate accurate outputs with machine learning. It has enabled companies to make informed decisions critical to streamlining their business operations. Such data-driven decisions help companies across industry verticals, from manufacturing, retail, healthcare, energy, and financial services, optimize their current operations while seeking new methods to ease their overall workload.

As computer algorithms become increasingly intelligent, we can anticipate an upward trajectory of machine learning in 2022 and beyond.

Knowledge

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