Applications of machine learning

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Applications of machine learning

What is ML application?

As we move forward into the digital age, One of the modern innovations we’ve seen is the creation of Machine Learning. This incredible form of artificial intelligence is already being used in various industries and professions.

Applications of machine learning
Source: Javatpoint

What are the major applications of machine learning?

1. Virtual Personal Assistants

Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. All you need to do is activate them and ask “What is my schedule for today?”, “What are the flights from Germany to London”, or similar questions. For answering, your personal assistant looks out for the information, recalls your related queries, or send a command to other resources (like phone apps) to collect info. You can even instruct assistants for certain tasks like “Set an alarm for 6 AM next morning”, “Remind me to visit Visa Office day after tomorrow”.

Machine learning is an important part of these personal assistants as they collect and refine the information on the basis of your previous involvement with them. Later, this set of data is utilized to render results that are tailored to your preferences.

2. Fraud Detection

Experts predict online credit card fraud to soar to a whopping $32 billion in 2020. That’s more than the profit made by Coca Cola and JP Morgan Chase combined. That’s something to worry about. Fraud Detection is one of the most necessary Applications of Machine Learning. The number of transactions has increased due to a plethora of payment channels – credit/debit cards, smartphones, numerous wallets, UPI and much more. At the same time, the amount of criminals have become adept at finding loopholes.

Whenever a customer carries out a transaction – the Machine Learning model thoroughly x-rays their profile searching for suspicious patterns. In Machine Learning, problems like fraud detection are usually framed as classification problems.

3. Image Recognition

It is one of the most common machine learning applications.There are many situations where you can classify the object as a digital image.

For digital images, the measurements describe the outputs of each pixel in the image. In the case of a black and white image, the intensity of each pixel serves as one measurement. So if a black and white image has N*N pixels, the total number of pixels and hence measurement is N2.

In the coloured image, each pixel considered as providing 3 measurements of the intensities of 3 main color components i.e. RGB.

For face detection – The categories might be face versus no face present. There might be a separate category for each person in a database of several individuals.

For character recognition – We can segment a piece of writing into smaller images, each containing a single character.  The categories might consist of the 26 letters of the English alphabet, the 10 digits, and some special characters.

4. Speech Recognition

Speech recognition (SR) is the translation of spoken words into text. It is an interdisciplinary subfield of computing and linguistics that develops methodologies and technologies that enable the popularity and translation of speech into text by computers.

Voice recognition may be a biometric technology wont to identify a specific individual’s voice or for talker identification. It is also known as “automatic speech recognition” (ASR), “computer speech recognition”, or “speech to text” (STT).

In speech recognition, a software application recognizes spoken words. The measurements in this Machine Learning application might be a set of numbers that represent the speech signal.

Speech recognition, Machine Learning applications include voice user interfaces. Voice user interfaces are such as voice dialing, call routing, domotic appliance control. It can also use as simple data entry, preparation of structured documents, speech-to-text processing, and plane.


5. Medical Diagnosis

ML provides methods, techniques, and tools that can help in solving diagnostic and prognostic problems in a variety of medical domains. It is being used for the analysis of the importance of clinical parameters and of their combinations for prognosis.

ML is also being used for data analysis, such as detection of regularities in the data by appropriately dealing with imperfect data, interpretation of continuous data used in the Intensive Care Unit, and for intelligent alarming resulting in effective and efficient monitoring.

It is argued that the successful implementation of ML methods can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care.

In medical diagnosis, the main interest is in establishing the existence of a disease followed by its accurate identification. There is a separate category for each disease under consideration and one category for cases where no disease is present.

6. Classification

Classification is a process of placing each individual from the population under study in many classes. These are identified as independent variables.

Classification helps analysts to use measurements of an object to identify the category to which that object belongs. To establish an efficient rule, analysts use data.

Data consists of many examples of objects with their correct classification.

For example, before a bank decides to disburse a loan, it assesses customers on their ability to repay the loan.

By considering factors such as customer’s earning, age, savings and financial history we can do it. This information is taken from the past data of the loan.

7. Prediction

“Prediction” refers to the output of an algorithm after it’s been trained on a historical dataset and applied to new data when forecasting the likelihood of a specific outcome.

Predict function are often applied to predict outcomes using the model.

Prediction are often done during model creation, after model creation, or after a failure (as long as a minimum of 1 iteration is finished).

Consider the example of a bank computing the probability of any of loan applicants faulting the loan repayment. To compute the probability of the fault, the system will first need to classify the available data in certain groups. It is described by a set of rules prescribed by the analysts.

Once we do the classification, as per need we can compute the probability. These probability computations can compute across all sectors for varied purposes The current prediction is one of the hottest machine learning algorithms.

8. Product Recommendations

You shopped for a product online few days back and then you keep receiving emails for shopping suggestions. If not this, then you might have noticed that the shopping website or the app recommends you some items that somehow matches with your taste. Certainly, this refines the shopping experience but did you know that it’s machine learning doing the magic for you? On the basis of your behaviour with the website/app, past purchases, items liked or added to cart, brand preferences etc., the product recommendations are made.

9. Online Customer Support

A number of websites nowadays offer the option to chat with customer support representative while they are navigating within the site. However, not every website has a live executive to answer your queries. In most of the cases, you talk to a chatbot. These bots tend to extract information from the website and present it to the customers. Meanwhile, the chatbots advances with time. They tend to understand the user queries better and serve them with better answers, which is possible due to its machine learning algorithms.

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