Applications of Machine Learning

Introduction

Artificial Intelligence and Machine learning are the two terms that are making a lot of buzz in the IT world. These technologies are helping almost every company to streamline complex processes, and work smarter by discovering data required to make better business decisions.

What is Machine Learning?

in 1959, Arthur Samuel coined the term Machine learning. In simple terms, it is a subset of AI (Artificial Intelligence) that gives the ability to learn from user behaviour and improve their experiences without any need for coding. For example: When you tend to purchase a product from a website, it also shows recommendations such as, “People who bought this.”

The machine learning applications work based on the good quality data supplied to the machine and with the help of various algorithms ML models are created to work according to this data fed to machines. With the help of the innate intelligence of ML, tasks can be automated just like humans perform.

Basically, three main uses of ML are:

• Detection- It is more about interpreting the present in the form of data fed to machines.
• Prediction– It is a simplistic way to the future and forecast it accurately. 
• Generation– All the creative tasks such as music, text, or images come under this category.

Applications of Machine Learning

In the Tech world, there are interminable applications of machine learning from very simple to complex. It performs excellently in cognition between computers, software, devices, and data fed into machines. Following are a few applications that we are using in our day-to-day life and perhaps we were not aware, but they are driven by ML.

1. Image Recognition

Does your phone simply unlock by looking at it? The answer is yes, and it is all because of the most common machine learning applications. It works based on face detection and recognition algorithms, and pattern recognition to identify the visual. The high-end camera of the mobile phone recognizes 80 nodal points of a human face and advanced ML technologies to count the variable of the face and ultimately unlocks the phone by a single look.

You can refine your search of photographs by simply writing an object name on the phone, which is also supported by ML. All you need to type is sea, tree, or dog, you will get all the photographs that include the specified object name in the images.

2. Voice Recognition

Also, known as Virtual Personal Assistants, some of the most popular examples are Siri, Alexa, Cortona, and others. As the name defines, it helps in finding the information as asked over the phone. You can ask questions such as “Name the longest road in the world?”, “what is my today’s schedule?” or other questions. Assistants will search for the information from the respected resources to collect all the required information to answer the question. Also, it helps in setting up an alarm or reminders based on the requirement. There are quite a lot of devices available in the market, but the Amazon Echo and Google Nest are the best options available for smart speakers.

3. Commuting Predictions

When it comes to Machine learning uses in travelling, the following are the applications:

• Traffic predictions – Almost everyone is using a GPS navigation system for travelling these days. All our current locations and the speed of the vehicle are stored on a central server. When we use GPS to check our route from a source to a destination, the app will automatically show the various ways to travel also, we can keep an eye on the congestion. ML prevents traffic congestion according to the analysis of the daily commuters.
• Online Transportation Networks – When you are using Uber or Ola, the App estimates the real-time location, price, and duration of the ride. Not only this, Machine learning has helped these apps in identifying congestion of traffic where can be found on the way, so the surging is also visible, or driver can detour for an appropriate price.

4. Videos Surveillance

Consider a situation in which a person monitors 20-30 cameras at a time. Undoubtedly it is boring and slips and lapses are undeniable. ML Applications are saviours in this case. The video surveillance systems are powered by AI which helps in detecting crime before it could happen. It tracks unusual behaviour such as stationery unclaimed objects for a long time, napping of drivers, and others. The system will generate alerts to the human personnel working there to negate the mishap. All such activities are reported and help in improving surveillance services with the help of Machine Learning performing at its best.

5. Social Media Platform

Machine Learning has stepped into our Social media accounts as well. It helps in personalizing news feeds and better ad targeting. Following are a few examples to understand in detail.

• People you may know: It works on a very simple concept of understanding the experiences or daily activities. Facebook continuously keeps track of the profiles you visit most, interests, or groups. Based on its learning, a list is prepared for Facebook users for suggestions to increase the number of friends.
• Face recognition: When you upload an image or you are tagging multiple friends, Facebook can immediately recognize the friend. Facebook keeps track of the projections and unique features while matching them with people in the friend list for suggestions.
• Similar Pins: Computer Vision is the core element of ML. It is a technique to extract useful information from images or videos and then amass similar pins as per the user’s query.
• Spam and Malware: In the Gmail account, when an email is received it is categorized as primary, Social, or Spam. An important mail automatically gets delivered to the inbox and the spam emails in the Spam box. It is done with the help of ML. Gmail uses spam filters such as Content filters, Rule-based filters, permission filters, and many more. Likewise, for malware detection, many algorithms are used such as multi-layer perceptron, Naïve Bayes classifier or Decision tree are a few of many used to detect malware before it may cause any impact.

6. Customer Support

Nowadays, many websites or companies provide an option of chatting with the customer support representative of the company. Most of the time, instead of humans, chatbots are the ones who answer multiple queries posted by customers. A chatbot is the best application of Machine learning that extracts information from the website and gives answers quickly and appropriately.

7. Product Recommendations

Many reputed companies such as Netflix or Amazon use Machine Learning for product suggestions which is far better than manual interventions. Whenever we search for any product or movie on the respective platforms, suggestions are shared for the product based on the interests of the user while surfing the internet with the help of machine learning.

8. Self-driving Cars

Another exciting application of ML is self-driving cars. Tesla is the most popular car manufacturing company for autonomous cars (self-driving cars) It uses an unsupervised learning method to drive the car and stop at a distance if a person or object is detected while driving. It is just like driving innovation in the changing automotive world.

9. Online Fraud Detection

Online transactions are the most vulnerable to cyber-attacks and to ensure safety and security, ML dives in to detect any fraudulent transactions. Whenever a person performs any transactions, chances are phishers are also performing fraudulent transactions with fake accounts. So, to curb that possibility, the Feed Forward Neural network keeps a check on fraud transactions. For each genuine transaction, hash values are produced and that gets changed in a specific manner for fraud transactions which makes our online transactions reliable.

Conclusion

The above-mentioned machine learning application examples are coming more into use as the digital world progresses and innovative ideas are openly accepted. It is one of the great things in the field of AI and it has simplified our day-to-day life. More and more organizations are moving towards ML and investing huge amounts to turn mundane processes, work faster and smoother. With the long list of applications, Machine learning is surely proving its potential and benefitting a lot of companies with its value. Also, it won’t be wrong to say that it is one of the widely used and adopted technologies in the era of the tech world.

FAQs

Q.1: What are the real-world applications of machine learning?

Ans. The real-world applications of Machine learning are:-

• Facebook
• Google
• Amazon
• Echo
• Paypal
• Uber
• Flipkart
• IBM
• Salesforce
• iPhones and many more

Q.2: How is machine learning used in industry?

Ans. Machine learning is a subset of Artificial Intelligence in which computers independently learn which is explicitly not programmed. Computers learn from their experience by leveraging algorithms, insights from data, and discovering patterns. Machines are not programmed, and they perform tasks on their own.

Q.3: What are the technologies used in machine learning?

Ans. The following are the best technologies used in Machine learning:

• No-Code Machine Learning.
• Unsupervised ML.
• TinyML.
• Reinforcement Learning.
• AutoML.
• Full-stack Deep Learning.
• Machine Learning Operationalization Management.
• Generative Adversarial Networks.

Q.4: Which one is the best tool for machine learning?

Ans. In the market, there are various machine learning tools such as PyTorch, KNIME, TensorFlow, and Weka, but the best one is Scikit-learn. Scikit-learn supports almost all the platforms and it is written in Python, Cython, C, and C++. Also, it supports almost all algorithms and features required for Machine Learning being free of cost.

Additional Resources

• Practice Coding
• Best Data Science Courses
• Best Machine Learning Courses
• Machine Learning Interview Questions
• Data Science Interview Questions
• Machine Learning MCQ
• Types of Machine Learning
• Machine Learning Projects
• Machine Learning Books