Machine learning is changing the landscape of how technology understands humans.

This form of artificial intelligence (AI) is an advanced discipline that cultivates data, allowing computers to learn about trends and behaviors and do the work on their own with minimal human intervention.

If it is your first time hearing about machine learning and what it can do for your business, this article helps you dissect its importance for innovation and business growth.

what is machine learning

What is machine learning?

Machine Learning (ML) is a form of discipline under AI. It provides computers with the methods to improve their ability to learn and understand data based on patterns and predictions.

With ML, machines operate automatically and autonomously without interference from humans. As ML software receives large volumes of information, they cultivate this data, learns from it independently, and adapts from it.

ML allows the identification of patterns and adapts to changing processes through algorithms.

ML offers development to data analysis with its complex mathematical calculations that automatically grow in volumes and varieties.

This development is highly beneficial for different industries that deal with bulky data.

How does machine learning work

The process of machine learning starts with supplying data and identifying the patterns that lie within it. These patterns are later saved as references, allowing machines to learn from them and adjust to changes without human assistance. It then creates a model through a trained algorithm, which permits predictions.

Now, these predictions are checked for accuracy within the machine, as ML algorithms have a dataset that deploys or trains the forecast repeatedly until it reaches the accurate insight.

Machine Learning vs. Deep Learning

Machine Learning vs. Deep Learning

Machine learning is associated with deep learning; however, these two are different. Machine learning aims to understand the data structure, learn from it, and create automation.

Meanwhile, deep learning falls under the machine learning category.

It is a more specific type of AI that combines advanced computation and specialized neural networks to learn complicated patterns from bulky data.

Types of Machine Learning

Types of Machine Learning

Machine learning has different types that serve various purposes. These are meant to understand data but differ in how the process is executed.

Supervised Learning

Machines are trained through labeled datasets, enabling output-based predictions.

The devices are incorporated with input sets and the corresponding expected output—they act as the teacher and trains the machine to make estimates out of the data given.

This type of ML is classified into two—classification and regression.

The former refers to algorithms that provide a solution to classification problems with the categorical output variable.

Meanwhile, the former fixes regression issues, especially for the linear connection of both the input and output variables.

Unsupervised Learning

Unsupervised learning is a technique without an unlabeled dataset. It allows output prediction without command or training, directing unsorted datasets to group themselves according to various factors.

This type of ML has two classifications—clustering and association.

Clustering groups object into clusters based on the identified parameters such as patterns, differences, and similarities.

On the other hand, the association is about identifying relations between the variables and learning about their dependency, and mapping the data on associated variables.

Semi-supervised learning

Semi-supervised learning combines supervised learning and unsupervised learning. It uses a mix of labeled and unlabeled datasets to train the machine’s systems and disables the challenges in both learning techniques.

Reinforcement Learning

Reinforcement learning draws feedback during the process by automatically storing data out of the mistakes, taking action, and learning from the experience for better performance.

This learning champions rewards for good performances for an agent, which is the machine, and penalizes wrong moves.

Lacking a labeled dataset and relying only on experiences, this technique aims to gain a high score.

Machine learning is widely used in a variety of industries

The development of machine learning as a form of AI continues to serve various industries and improve their processes.



Manufacturing industries can now rely on condition monitoring and predictive maintenance for their facilities and plants without needing on-site workers operating the work.



The healthcare industry has benefitted from machine learning with the advent of wearable devices that tracks a person’s health determinants.

Fitness trackers and smart health watches are examples of products that have adapted to machine learning, with real-time monitoring and assessing of health.

Aside from this, ML is an advantage for doctors and scientists to analyze trends in patient care, including diagnostics and treatment.

Precisely, medical practitioners can accurately determine one’s life span due to ML’s prediction of trends.

Financial services

Financial services

Financial institutions such as banks use this technology and their cyber-surveillance systems to fight fraudsters and develop insights from big data that can identify loopholes in their processes.



Machine learning is a massive factor for the retail industry in learning about customers’ buying behavior, allowing them to adapt to changing needs.

ML is also a massive boost for companies’ marketing campaigns, analyzing customer insights, planning for merchandise, and price changes.

ML can also provide advanced services like natural langue processing, chatbots, and virtual assistants that automate a customer’s experience.

Travel Industry

Travel Industry

ML algorithms allow the travel industry to offer rides with dynamic pricing. A typical example is Uber, which identifies your travel pattern, the supply and demand of an area, and the suitable price range.

Additionally, the travel industry looks into reviews of consumers, allowing them to adjust their campaigns and promotions.

Examples of machine learning

Examples of machine learning you might encounter

Machine learning is now everywhere. This technique is already adopted by retail, medical, and financial institutions to tackle data automatically and accurately.

Search engines

Search engines use machine learning to study users’ behavior and learn about their preferences to fuel recommendations for them.

Medical diagnosis

Medical devices and machines are powered with ML to spot markers of illnesses such as cancer and organ failure. Aside from capturing images, it detects spots of information present in lab findings.

Online fraud detection

Financial institutions use machine learning to detect suspicious activities and fraudulent transactions. ML gives banks and customers a safer and more secure transaction environment.

Self-driving cars

Self-driving cars hold technology from machine learning, specifically deep learning, for it to be able to perform its uses accurately and safely.

Image recognition

Image recognitions lets businesses analyze images to identify people or tell differences.

Speech recognition

Machine learning uses natural language processing (NLP) to translate human speech into text. Examples of this are voice assistants such as Apple’s Siri and Amazon’s Alexa.

How can machine learning be used in education?

Every industry that deals with complicated processes can benefit from machine learning. If you are in the education sector, you can use ML to provide automation for faster and more convenient operations.

Sumadi can help you incorporate machine learning into your business, especially online assessments or examinations with effective proctoring solutions.

AI and machine learning power up our online assessment technology to derive accurate insights from trends and ensure accurate results with integrity. If you want to know more about machine learning   and how Sumadi can help you, book a consultation with us to get started.