Applications of Machine learning in the Real World

Applications of Machine learning in the Real World

Machine learning is a modern invention that has improved our daily lives along with many professional and industrial processes. ML is a subset of AI technology that focuses on using statistical methods to design intelligent computer systems to learn from available databases. This wonderful technology helps to improve business decisions, boost productivity, detect disease, forecast the weather, and much more.

Machine learning is used in many real-world applications for a variety of purposes in a variety of industries. In this blog post, we will look at the various applications of machine learning.

1. Image Recognition

Image recognition is one of the most common uses of machine learning in the real world. It can detect an object as a digital image depends on the intensity of the pixels in color images or black and white images.

Following are the three real-world examples of image recognition:
  • Name the photo taken ("tagging" on social media)
  • Label an x-ray as cancerous or not
  • Identify handwriting by dividing a single letter into smaller images

Machine Learning technology is also often used for facial recognition within an image. Using a database of indiv system can recognize commonalities and compare them to faces. This technology is majorly used in law enforcement.

2. Commute Predictions

Traffic Management: Nowadays, it is common that people are using GPS (Global Positioning System) technology to navigate while travelling. In such cases, machine learning can help us avoid traffic and find shortcuts to reach our destination on time. Programmed GPS works, when we use it to navigate, it saves our locations and speed on the Central Server of Traffic Management, which is then used to create a current traffic map. Machine learning does congestion analysis this way, the only problem is that it is sometimes incorrect to use GPS when driving a smaller number of cars.

Online Transport Applications: ML helps estimate the price and estimated time arrival (ETL) of online cab booking apps like Old, Uber, Lyft, etc., while we book the car.

3. Speech Recognition

Speech recognition is the translation of spoken words into text. It is also called as Automatic Speech Recognition (ASR), Speech to Text (STT) or Computer Speech Recognition (CSR).

A software application recognizes the spoken words in speech recognition. The measurements in this app can be a set of numbers indicating the speech signal. We can divide a signal into parts that have different sounds or words.

We can indicate the speech signal in each segment by energy or intensities in different time-frequency bands. Although the details of the signal representation are beyond the scope of this article, we can refer to the signal by a set of actual values.

Speech recognition apps include voice user interfaces and voice user interfaces consist of call dialling, call routing, and control of the domestic appliance. It can also be used for speech-to-text processing, general data entry, aircraft, and structured document preparation.

4. Medical Diagnosis

ML technology can be used to help diagnose of diseases. In the health sector, many physicians use chatbots with speech recognition capabilities to identify patterns in symptoms.

Following are the three real-world examples of medical diagnosis:
  • Analyze body fluids
  • Recommend a treatment option or assist in making a diagnosis
  • Pathology and oncology use machine learning technology to detect cancerous tissue

In the case of rare diseases, the combined use of machine learning facial recognition software can help to scan patient images and identify symptoms associated with rare diseases.

5. Fraud Detection

Financial institutions and banks are using machine learning technology to detect fraud, which helps to keep customers secure. ML technology can also help companies detect fraudulent credit card transactions.

ML in finance can create super-accurate predictive maintenance models automatically to detect and prioritize all types of fraudulent activities. Businesses can create a data-based queue and investigate based on priority levels. This enables you to diversify resources in the place where you see great returns on investigation costs. In addition, it aids you to improve customer experience by protecting their bank accounts.

Detecting such fraud using machine learning technology can help financial institutions and banks save money on chargebacks/ disputes, as machine learning models can be trained to flag fraudulent transactions based on specific features.

6. Voice Assistants

Voice assistants are everywhere now. Famous voice assistants such as Amazon's Alexa, Google Assistant, and Apple's Siri are paving the way for people to become part of the general conversation. ML algorithm behind these voice assistants helps to recognize human speech using NLP technology.

Then, using machine learning, it converts speech into numbers and generates responses accordingly. In addition, Natural Language Processing is used to translate ambiguous legitimacy into the normal language to prepare information. As machine learning techniques become more advanced, researchers hope that this technology will be amazingly smart in the future.

7. Personalized Marketing

Technology is tremendously growing in the marketing system. Using ML features, the marketing field segments consumers depending on characteristics and behavioural data. Digital marketing platforms enable marketers to concentrate on the audience set with relevant product impact. So that, they understand customer needs and provide services with better product promotion accordingly


As machine learning technology continuously evolve, the range of applications and use cases of machine learning also expands. In this new decade, it is worth focusing on how machine learning applications can be implemented in business domains to effectively navigate business issues, improve efficiency, reduce costs, and provide better customer experiences.

However, in order to deploy machine learning technology in your organization and increase productivity, it is crucial to partner with a reliable machine learning service provider. At Quadrant Resource, we provide machine learning solutions that help us understand the complexity of different business issues, identify gaps, and provide efficient and effective solutions to handle these challenges.

To learn more about our machine learning solutions, please contact