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What is Machine Learning??? What is the use of machine learning in real life???

Know About Machine Learning (ML)

Machine Learning (ML) is a domain of Artificial Intelligence (AI) that enables a learning system to learn from historical data and predict possible outcomes with minimal human intercession. Machine learning algorithms are widely utilized to execute necessary tasks in a variety of industries, including health care, automation, logistics, speech recognition, computer vision, data analytics, financial services, and many more.

A machine learning system can carry out any task even when they are not specifically trained to do so. In general, computers can accomplish any simple task assigned to them without prior learning or training by simply programming the algorithms to execute all of the required steps to solve the issue.

Machine Learning (ML) approaches are broadly categorized into three types, based on the feedback available to the learning system. They are as follows.

  1. Supervised Learning
  2. Unsupervised Learning
  3. Reinforcement Learning

Types of Machine Learning

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Supervised Learning

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Unsupervised Learning

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Reinforcement Learning

Supervised Learning

Before going into detail, let me keep the concept simple. Usually, supervising implies keeping a watch on something while it is being performed. We can find this kind of learning at schools. Generally, the teacher offers all inputs and objectives, as well as supervises the child during the learning process. Similarly, a computer system is instructed to formulate a general mathematical rule that includes both inputs and intended outputs under supervision.

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Supervised Learning (SL), often known as Supervised Machine Learning (SML), is a field in artificial intelligence. The main objective of this approach is to develop a learning system that can map given inputs to desired outputs and predict outcomes for given inputs. The information provided to the learning system is referred to as “Training Data,” and it contains numerous training examples. Active learning, regression, and classification are the types of supervised learning algorithms.

Unsupervised Learning

The name itself emphasizes that this is a learning process where no one assists while performing any activity. We are all familiar with the phrase “Self-paced Learning,” which indicates that just input materials are provided but no objectives (or) output are supplied with the teacher’s supervision. Individuals must work with the available material to generate their own results. Similarly, in unsupervised learning, the computer system has to discover patterns and similarities for the given input to gain the output.

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Reinforcement Learning

Reinforcement Learning (RL) is a method in which the learning system uses trial and error to make conclusions about any problem. The system is trained to make a sequence of decisions in complicated circumstances using this approach. The learning system is not given any resources, but it is motivated by either rewards or penalties. Feedback is exchanged with the systems and rewards are based on performance. The ultimate objective is to provide appropriate solutions to the problem while minimizing the rewards.

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Below listed are a few real-time examples of Machine Learning (ML)

Search Engine Results

Previously, books were thought to be the best resources for extracting information, but today it is common to notice that the internet has taken over everything. Whenever we confront any problem (or) a query, we look for solutions on the internet using numerous web browsers. It displays thousands of results in a fraction of a second while recommending appropriate results. This approach may seem to be straightforward, however search engines use several processes in the background to display the results.

When you type “fitness,” for example, the search engine now crawls for information and indexes URLs based on keywords, accuracy, and content related to the given input. Finally, the search engine displays the web pages based on their search algorithms. Machine language plays a major role in the process of crawling, indexing, organizing, and displaying results. These days most search engines like Google, Bing, Internet Explorer, Firefox, etc employ machine learning to give the best results for their users.

Automatic Cars

The evolution of technology is all around us, and the notion of “self-driving automobiles” has made a tremendous influence on the automotive industry. There is no doubt that these vehicles will be the transportation industry’s future. Machine learning and artificial intelligence are replacing traditional computer vision algorithms in self-driving automobiles.

These self-driven cars employ neural networks for monitoring the driver and alerting him/her based on facial expressions to enhance security. Several sensors are implanted to gather data from its surroundings and interpret the results. The three major sensors, cameras, radar, and lidar work just like the human brain and eyes to understand the surroundings and identify the location, 3D objects, and speed.

Voice Assistants

Voice assistants, which are widely used now, are not new. Yes, you read it correctly: the first voice-activated toy, “Radio Rex,” was launched in 1922. “Rex” the dog stays inside the house until called by the user. This is a simple toy with a basic mechanism that uses voice recognition. Similarly, the voice assistants use voice recognition, language processing algorithms, machine learning, artificial, and voice synthesis to perform specific functions as per users’ commands.

Today, digital assistants like Google Assistant, Siri, Alexa, etc are integrated with various devices like cell phones, computers, and smart speakers. When a command is given to these voice assistants they can take an entire sentence as an input rather than single words. They convert the audio to text and vice versa to respond to the given command.

Face Recognition

Face Recognition technology is used to identify any person only by looking at them. Machine learning is employed here to identify, collect, store and match the characteristics of the people with their database. Deep learning convolutional Neural Networks (CNN) are used for facial recognition.

Basically, facial recognition uses Machine Learning (ML) with Artificial Intelligence (AI) to identify human faces from the background. Firstly, this process starts by searching human eyes, followed by eyebrows, mouth, nostrils, and the rest of the facial features. Usually, this technology extracts a pattern through your facial features and analysis it. This is how machine learning is helpful in face recognition.

Healthcare

Machine learning has brought a huge impact on the field of medical science. This technology is helpful in identifying diseases and analyzing them. As the healthcare industry holds huge customer information, it is very important to secure and manage the data. Machine learning helps to gather, organize, store, and manage customer data.

Besides, it also helps in monitoring health conditions and predicting diseases like heart strokes, diabetes, hypertension, and so on in the early stages. Medical assistance is provided to the patients virtually and follow-up treatment between doctor visits. Machine learning helps in suggesting personalized medical treatments by analyzing patients’ medical histories. Finally, the finest feature is that it can analyze and correct errors in medical prescriptions. Machine learning extensively examines medical data and patient prescriptions to detect and correct any errors.

Agriculture

The main objective of implementing ML in agriculture is to minimize overall production losses, identify weeds and diseases, and increase crop productivity. Machine learning (ML) can be employed in all phases of agriculture, from pre-harvesting through post-harvesting. ML aids in the analysis of soil fertility, pesticide use, seed quality, weed identification, and environmental conditions during the pre-harvesting stage.

A farmer can obtain information on crop size, skin colour, flavor, maturity stage, and firmness during the harvesting stage. Furthermore, machine learning recognizes many elements that might impact crop production, such as temperature, humidity, seasonal fluctuations, and so on, at the last stage, i.e. post-harvesting. Most agricultural fields, including horticulture, forestry, livestock, and fishing, can benefit from machine learning.

Moreover, machine learning is widely used in a wide variety of fields, including traffic alerts via Google Maps, chatbot (online customer service), Google translation, email intelligence, banking and finance, and many more.

To conclude, machine learning is a powerful tool for designing and developing algorithms that can learn and produce accurate predictions from acquired data. Machine learning is rapidly expanding in the field of computer science. Machine learning is already being used to handle challenging issues that humans cannot solve in a wide range of industries. It will be amazing to observe the influence of machine learning and how it’s going to affect our lives over the next 20 years.

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