Is Your Enterprise Well-Prepared for AI?

DZone 's Guide to

Is Your Enterprise Well-Prepared for AI?

In this article, we will look at the various types of machine learning methods and explore why businesses need machine learning.

· AI Zone ·
Free Resource

With every passing day, businesses are seeking to automate their processes. Organizations have implemented automated processes in customer service, production, marketing, data collection, and healthcare to name a few. At the core of automation sits machine learning. Machine learning is a subset of AI, based on the concept that systems/machines can learn from data and identify patterns to make decisions with minimal human intervention.

Machine learning is helping organizations to enhance their growth and optimize their business processes such as manufacturing, customer service, supply chain management, and marketing. Moreover, it improves employee engagement and enhances customer satisfaction.

In this article, we will look at the various types of machine learning methods and explore why businesses need machine learning.

Classification of Machine Learning Methods

In general, Machine Learning algorithms can be classified as either "supervised" or "unsupervised" learning. The term "supervise" here does not refer to human intervention but rather the existence of a training dataset with the "correct" values for the machine to learn from. We can further categorize machine learning methods based on its purpose and the system it is applied to. They are as follows:

Supervised Learning: In this method, algorithms train with the help of labeled examples, such as an input where you know the desired output. For instance, an equipment could have data points labeled either “F” (failed) or “P” (pass). The learning algorithm gathers a set of inputs and corresponding correct outputs. The algorithm learns by comparing its actual output with the right outputs to find errors. It then modifies the model accordingly.

Unsupervised Learning: One can use this method against data that lacks historical labels. The user does not provide the system with the "correct answer." The algorithm is expected to figure out what is being represented. The aim is to explore the data and find a structure within. Unsupervised learning works well on transactional data. It is popularly used for marketing campaigns as it helps marketers segment their customers based on similar traits.

Semi-supervised Learning: Its application and purpose are the same as those of supervised learning. However, it uses both unlabeled and labeled data for training. It typically includes a small amount of labeled data with a significant amount of unlabeled data. Organizations might choose to go with this method as acquiring and analyzing labeled data involves substantial costs. Semi-supervised learning facilitates prediction, regression, and classification.

Reinforcement Learning: Although similar to supervised learning, reinforcement learning is focused on maximizing rewards rather than just getting "correct" answers to a problem. This method helps to incorporate machine learning into gaming, robotics, and navigation. Algorithms discover the next point of action through trial and error and decide which actions yield the greatest rewards. Reinforcement learning is comprised of three primary components:

  • Agent: It refers to the decision maker or the learner
  • Environment: It includes everything the agent interacts with
  • Actions: It involves all that the agent can do

Moving on, let us look at how machine learning is helping businesses across industries.

Why Businesses Can't Afford to Miss the Machine Learning Bus

Machine learning not only helps organizations to streamline their processes, but it also helps improve production efficiency with reduced costs and enhanced precision. Below are some machine learning advantages that make it imperative for businesses to adopt it.

  • Smoother Supply Chain Management: Organizations that are into mass production, transportation, and retail often face issues with their supply chain management. Adding to the vows of businesses is the interdependency of supply chain management on every connecting factor. A delay in one element results in the overall postponement of the entire process. Machine learning helps organizations with the contextual analysis of logistics data to predict and mitigate supply chain risks.
  • Personalizing Customer Service: One can trace the key to a satisfied customer to an efficient customer care service. Thanks to machine learning, organizations have introduced chatbots which are replacing humans. Chatbots combine historical customer service data, NLP, and algorithms to interact in an almost human form in a much faster manner.
  • Shortlisting Relevant Candidates: In a survey conducted by ideal, more than 50% of recruiters said that shortlisting the correct candidate was the most challenging part of their job as recruiters. The modern-day software uses machine learning to swiftly scan through thousands of job applications and shortlist relevant candidates. It can also automatically flag biased language in job descriptions and detect suitable candidates overlooked by recruiters because they didn't fit traditional expectations.
  • Fraud Detection: In its 2018 Global Economic Crime and Fraud Survey, PwC observed that 49% of global organizations had experienced economic crime and fraud. With the help of building models based on social network information, historical transactions, and other external sources of data, machine learning algorithms can use pattern recognition to spot exceptions, anomalies, and outliers. Organizations can apply machine learning algorithmic security to a wide range of situations.
  • Measuring Brand Exposure: Measuring a brand's exposure helps organizations assess the brand awareness of a product or service. However, this is easier said than done. While doing so, organizations often make common mistakes. When businesses automate the program to measure a brand exposure, the results are faster, relevant, and more accurate. Using advanced image recognition, users can track the brand logo positions that appear in video footage of a sporting event, such as a football game.


AI and Big Data are transforming the way we live our lives and the way industries function. Businesses are looking out for innovative ways to increase the efficiency of their business processes and ultimately enhance their business growth. Machine learning is one such technological innovation that is at forefront of the application of technology in businesses. It is a solution formed by the integration of AI and Big Data.

In this article, we looked at the basic concept of machine learning, its various methods, and the business benefits of machine learning.

algorithm ,trends ,big data ,artificial intelligence ,machine learning ,supervised machine learning ,unsupervised machine learning

Published at DZone with permission of Leona Zhang . See the original article here.

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}