Importance of Machine Learning

Machine Learning is one of the most popular sub-fields of Artificial Intelligence. Machine learning concepts are used almost everywhere, such as Healthcare, Finance, Infrastructure, Marketing, Self-driving cars, recommendation systems, chatbots, social sites, gaming, cyber security, and many more.

Currently, Machine Learning is under the development phase, and many new technologies are continuously being added to Machine Learning. It helps us in many ways, such as analyzing large chunks of data, data extractions, interpretations, etc. Hence, there are unlimited numbers of uses of Machine Learning. In this topic, we will discuss various importance of Machine Learning with examples. So, let's start with a quick introduction to Machine Learning.

What is Machine Learning?

Machine Learning is a branch of Artificial Intelligence that allows machines to learn and improve from experience automatically. It is defined as the field of study that gives computers the capability to learn without being explicitly programmed. It is quite different than traditional programming.

How Machine Learning Works?

Machine Learning is a core form of Artificial Intelligence that enable machine to learn from past data and make predictions

It involves data exploration and pattern matching with minimal human intervention. There are mainly four technologies that machine learning used to work:

1. Supervised Learning:

Supervised Learning is a machine learning method that needs supervision similar to the student-teacher relationship. In supervised Learning, a machine is trained with well-labeled data, which means some data is already tagged with correct outputs. So, whenever new data is introduced into the system, supervised learning algorithms analyze this sample data and predict correct outputs with the help of that labeled data.

It is classified into two different categories of algorithms. These are as follows:

• Classification: It deals when output is in the form of a category such as Yellow, blue, right or wrong, etc.
• Regression: It deals when output variables are real values like age, height, etc.

This technology allows us to collect or produce data output from experience. It works the same way as humans learn using some labeled data points of the training set. It helps in optimizing the performance of models using experience and solving various complex computation problems.

2. Unsupervised Learning:

Unlike supervised learning, unsupervised Learning does not require classified or well-labeled data to train a machine. It aims to make groups of unsorted information based on some patterns and differences even without any labelled training data. In unsupervised Learning, no supervision is provided, so no sample data is given to the machines. Hence, machines are restricted to finding hidden structures in unlabeled data by their own.

It is classified into two different categories of algorithms. These are as follows:

• Clustering: It deals when there is a requirement of inherent grouping in training data, e.g., grouping students by their area of interest.
• Association: It deals with the rules that help to identify a large portion of data, such as students who are interested in ML and also interested in AI.

3. Semi-supervised learning:

Semi-supervised Learning is defined as the combination of both supervised and unsupervised learning methods. It is used to overcome the drawbacks of both supervised and unsupervised learning methods.

In the semi-supervised learning method, a machine is trained with labeled as well as unlabeled data. Although, it involves a few labeled examples and a large number of unlabeled examples.

Speech analysis, web content classification, protein sequence classification, and text documents classifiers are some most popular real-world applications of semi-supervised Learning.

4. Reinforcement learning:

Reinforcement learning is defined as a feedback-based machine learning method that does not require labeled data. In this learning method, an agent learns to behave in an environment by performing the actions and seeing the results of actions. Agents can provide positive feedback for each good action and negative feedback for bad actions. Since, in reinforcement learning, there is no training data, hence agents are restricted to learn with their experience only.

Importance of Machine Learning

Although machine learning is continuously evolving with so many new technologies, it is still used in various industries.

Machine learning is important because it gives enterprises a view of trends in customer behavior and operational business patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google, and Uber, make machine learning a central part of their operations. Machine learning has become a significant competitive differentiator for many companies.

Machine learning has several practical applications that drive the kind of real business results - such as time and money savings - that have the potential to dramatically impact the future of your organization. In particular, we see tremendous impact occurring within the customer care industry, whereby machine learning is allowing people to get things done more quickly and efficiently. Through Virtual Assistant solutions, machine learning automates tasks that would otherwise need to be performed by a live agent - such as changing a password or checking an account balance. This frees up valuable agent time that can be used to focus on the kind of customer care that humans perform best: high touch, complicated decision-making that is not as easily handled by a machine. At Interactions, we further improve the process by eliminating the decision of whether a request should be sent to a human or a machine: unique Adaptive Understanding technology, the machine learns to be aware of its limitations, and bailout to humans when it has low confidence in providing the correct solution.

Use cases of Machine Learning Technology

Machine Learning is broadly used in every industry and has a wide range of applications, especially that involves collecting, analyzing, and responding to large sets of data. The importance of Machine Learning can be understood by these important applications.

Some important applications in which machine learning is widely used are given below:

1. Healthcare: Machine Learning is widely used in the healthcare industry. It helps healthcare researchers to analyze data points and suggest outcomes. Natural language processing helped to give accurate insights for better results of patients. Further, machine learning has improved the treatment methods by analyzing external data on patients' conditions in terms of X-ray, Ultrasound, CT-scan, etc. NLP, medical imaging, and genetic information are key areas of machine learning that improve the diagnosis, detection, and prediction system in the healthcare sector.
2. Automation: This is one of the significant applications of machine learning that helps to make the system automated. It helps machines to perform repetitive tasks without human intervention. As a machine learning engineer and data scientist, you have the responsibilities to solve any given task multiple times with no errors. However, this is not practically possible for humans. Hence machine learning has developed various models to automate the process, having the capability of performing iterative tasks in lesser time.
3. Banking and Finance: Machine Learning is a subset of AI that uses statistical models to make accurate predictions. In the banking and finance sector, machine learning helped in many ways, such as fraud detection, portfolio management, risk management, chatbots, document analysis, high-frequency trading, mortgage underwriting, AML detection, anomaly detection, risk credit score detection, KYC processing, etc. Hence, machine learning is widely applied in the banking and finance sector to reduce error as well as time.
4. Transportation and Traffic Prediction: This is one of the most common applications of Machine Learning that is widely used by all individuals in their daily routine. It helps to ensure highly secured routes, generate accurate ETAs, predict vehicle breakdown, Driving Prescriptive Analytics, etc. Although machine learning has solved transportation problems, it still requires more improvement. Statistical machine learning algorithms helps to build a smart transportation system. Further, deep Learning explored the complex interactions of roads, highways, traffic, environmental elements, crashes, etc. Hence, machine learning technology has improved daily traffic management as well as a collection of traffic data to predict insights of routes and traffic.
5. Image Recognition: It is one of the most common applications of machine learning which is used to detect the image over the internet. Further, various social media sites such as Facebook uses image recognition for tagging the images to your Facebook friends with its feature named auto friend tagging suggestion.
   Further, now a day's, almost all mobile devices come with exciting face detection features. Using this feature, you can secure your mobile data with face unlocking, so if anyone tries to access your mobile device, they cannot open without face recognition.
6. Speech Recognition: Speech recognition is one of the biggest achievements of machine learning applications. It enables users to search content without writing text or, in other words, 'search by voice'. It can search content/products on YouTube, Google, Amazon, etc. platforms by your voice. This technology is referred to as speech recognition.
   It is a process of converting voice instructions into the text; hence it is also known as 'Speech to text' or 'Computer speech recognition. Some important examples of speech recognitions are Google assistant, Siri, Cortana, Alexa, etc.
7. Product Recommendation: It is one of the biggest achievements made by machine learning which helps various e-commerce and entertainment companies like Flipkart, Amazon, Netflix, etc., to digitally advertise their products over the internet. When anyone searches for any product, they start getting an advertisement for the same product while internet surfing on the same browser.
   This is possible by machine learning algorithms that work on users' interests or past experience and accordingly recommend them for products. For e.g., when we search for a laptop on the Amazon platform, then it also gets started with so many other laptops having the same categories and criteria. Similarly, when we use Netflix, we find some recommendations for entertainment series, movies, etc. Hence, this is also possible by machine learning algorithms.
8. Virtual Personal Assistance: This feature helps us in many ways, such as searching content using voice instruction, calling a number using voice, searching contact in your mobile, playing music, opening an email, Scheduling an appointment, etc. Now a day, you all have seen advertising like "Alexa! Play the Music" this is also done with the help of machine learning. Google Assistant, Alexa, Cortana, Siri, etc., are a few common applications of machine learning. These virtual personal assistants record our voice instructions, send them over to the server on a cloud, decode it using ML algorithms and act accordingly.
9. Email Spam and Malware detection & Filtering: Machine learning also helps us for filtering emails in different categories such as spam, important, general, etc. In this way, users can easily identify whether the email is useful or spam. This is also possible by machine learning algorithms such as Multi-Layer Perceptron, Decision tree, and Naïve Bayes classifier. Content filter, header filter, rules-based filter, permission filter, general blacklist filter, etc., are some important spam filters used by Google.
10. Self-driving cars: This is one of the most exciting applications of machine learning. Machine learning plays a vital role in the manufacturing of self-driving cars. It uses an unsupervised learning method to train car models to detect people and objects while driving. Tata and Tesla are the most popular car manufacturing companies working on self-driving cars. Hence, it is a big revolution in a technological era which is also done with the help of machine learning.
11. Credit card fraud detection: Credit card frauds have become very easy targets for online hackers. As the culture of online/digital payments is increasing, the risk of credit/debit cards is parallel increasing. Machine Learning also helps developers to detect and analyze frauds in online transactions. It develops a novel fraud detection method for Streaming Transaction Data, with an objective to analyze the past transaction details of the customers and extract the behavioral patterns. Further, cardholders are clustered into various categories with their transaction amount so that the behavioral pattern of the groups can be extracted respectively. Hence, credit card fraud detection is a novel approach using Aggregation Strategy and Feedback Mechanism of machine learning.
12. Stock Marketing and Trading: Machine learning also helps in the stock marketing and trading sector, where it uses historical trends or past experience for predicting the market risk. As share marketing is another name of marketing risk, machine learning reduces it to some extent and predicts data against marketing risk. Machine learning's long short-term neural memory network is used for the prediction of stock market trends.
13. Language Translation: The use of Machine learning can be seen in language translation. It uses the sequence-to-sequence learning algorithms for translating one language into other. Further, it also uses images recognition techniques to identify the text from one language to other. Similarly, Google's GNMT (Google Neural Machine Translation) provides this feature, which is a Neural Machine Learning that translates the text into our familiar language, and it is called automatic translation.

Conclusion:

Machine Learning is directly or indirectly involved in our daily routine. We have seen various machine learning applications that are very useful for surviving in this technical world. Although machine learning is in the developing phase, it is continuously evolving rapidly. The best thing about machine learning is its High-value predictions that can guide better decisions and smart actions in real-time without human intervention. Hence, at the end of this article, we can say that the machine learning field is very vast, and its importance is not limited to a specific industry or sector; it is applicable everywhere for analyzing or predicting future events.