Top 15 Machine Learning Applications

11 min readDec 28, 2022

Machine learning (ML) is a part of artificial intelligence (AI) that allows computers to automatically use information and behaviors in order to identify patterns and predict upcoming events with a minimum of human communication. We cannot ignore why important the internet is to both our personal and professional life. Today, technology is a need for everyone. Nearly ten years ago, we only used human methods to achieve our goals and had no idea that, in the present, we would even consider the use of machine learning applications.

Machine learning is a popular topic in technology, and for good reason — it represents a significant advancement in the capacity of computers to learn. The rising demand for machine learning engineers is a result of developing technology and the production of large data, or Big Data.

Table of Contents

What is Machine Learning

The term “machine learning” refers to a number of practices and methods that allow computers to understand and modify themselves. Artificial intelligence can learn using machine learning methods without needing to directly instruct for this purpose. The machine learning algorithm identifies and executes activities based mainly also on obtained structure instead of a specified program command through understanding a structure using statements. In some conditions in which it becomes difficult to apply hard techniques, machine learning is able to help.

Applications of Successful Machine Learning

1. Social Media

Machine learning will be used in social media to identify information and classify it into groups. Social media technology uses computer technology to decide which features should be displayed to which audience. They Collect Data From The User And Analyze It For The Future Aspect With The Help Of Machine Learning. The messages, like, a collection of tests, images, videos, and sounds are all used in machine learning.

Machine learning, on the other hand, implements techniques to collect all types of information and send it to the users so they can carry out their tasks. In social media, analysis of the data and data classification from machine learning is necessary.

But there are A Few Purposes For Use Machine Learning In Social Media Monitoring, As well as:

Managing Automated Data — Lots of people are using social media every day, and it can be difficult to collect, share, and manage the various types of data. So, without human involvement, machine learning helps enable data structure.

Control Security– However, machine learning greatly helps in the detection of malware, trash, and backlinks on social media that are a risk to data and the entire organization.

Increasing Consumer Awareness Between several Target Markets– Machines are now able to choose which data or advertisements should be displayed to certain audiences in social media with the aid of machine learning technology. Increased information access and social media followers are massive benefits. It enables consumers’ information and processes to connect with the program’s advertisements and user behavior.

Improve the quality of the media– The media plays an important role In Social Media. The quality of images, audio, and videos on social media platforms can be auto-improved by using machine learning. In order to improve users’ visual information, Twitter and Facebook use machine learning.

2. Sentiment Analysis

One of the most crucial uses of machine learning is sentiment analysis. Sentiment analysis is just a natural machine learning technology that identifies a speaker’s or author’s sentiment or attitude. For example, a sentiment analyzer helps easily decide the intention or emotion of a post or email that has been created. A website with a replay system, statement apps, etc. may all be examined using this sentiment analysis system. written. This sentiment analysis tool can be used to examine decision-making applications, review-based websites, etc.

Here are some ways that sentiment analysis has used machine learning algorithms:

Attitudes regarding smartphone companies

As per research, depending upon 9000 reviewers, the sentiment through the five of well smartphone brands — Samsung, Apple, Huawei, Oppo, and Xiaomi — and various products has now been examined.

To test the quality of generating sentiment value, under this using Support Vector Machines (SVM), Multilayer Perceptron Neural Networks (MLP Neural Nets), Naive Bayes (NB), and Decision Tree (DT) methods.

Bitcoin sentiment and reduction in costs

Both Twitter and Facebook reviews for products offer useful data about user opinion. Facebook and Twitter were used to collect the information for this research. Recurrent Neural Networks (RNN) and Long-Short Term Memory (LSTM) techniques are used by studies to look into the link between changes in the price of bitcoin and social media sentiment.

Depending on the customer feedback, identifying product and shopping details

There are 142.8 million ratings in the database that will be used for examination, which has been collected from the websites of Amazon, ShopClues, and Flickr. To create a new hybrid review system(HRS), they use a model with machine learning to detect the actual sentiment of reviews

Mean absolute error, mean squared error, and mean absolute percentage error are also the three measures used to assess the system HRS quality. The result indicates that HRS’s absolute average error is 98%, which is a high level of accuracy.

3. Email Spam and Malware Filtering

Email systems implement a number of techniques for malware detection. These malware detectors were also supported with machine learning to verify that they have been up to date. It is impossible to keep up with hackers’ most recent dirty tricks while system malware detection is used. Several of the malware classifiers which use ML include Multi-Layer Activation functions and C 4.5 Classification Tree Introduction.

4. Search Engine Result Refining

In order to better user search results, Search engines such as google apply machine learning. The technologies inside the back screen how users react to the same results during each search users perform. The search engine believes that the results provided have been related to your query when users click the top results and browse that page for a long time. The search engine considers that the results provided didn’t match your demands when you read the second or third page of google results but you do not open any one of them. The machine learning techniques in at server increase your search results in this way.

We Know How Search Engines Use Machine Learning

Pattern Recognition
Detecting New Signals
It has a small portion capacity.
Special Signals Based on a Particular Query
Natural Language Processing,
Using Image Search to Identify Images
Improving Ad Quality & Targeting
Identity of Different meanings
Query Explanation

5. Products Recommendations

You already made an online purchase, and now you still get emails offering buying tips. In the absence of that, you may also have seen also that a shopping site or app offers many products that mostly match your preference. The online shopping is implemented by this, but did you understand that machine learning will actually work its trick in your best interests? depends on your use of the app or website transaction history, items you’ve bought or saved to your cart, brand identification, etc.

Imagine if you look at one product available on Amazon but decide to not immediately purchase it. But, when watching movies on YouTube that same day, an advertisement for the exact product shows up out of the blue. The same advertisement is displayed when you browse on Facebook. Then, how does it occur?

Google monitors your browsing history and makes advertising suggestions based on that, so that’s why it happens. That represents one of the most useful applications for machine learning. Product reviews actually account for 35% of Amazon’s profit.

6. Self-Driving Cars

This is a great example of a machine-learning application. People already use it because it is now available. Self-Driving cars depend entirely on machine learning, so I’m completely sure it is Tesla. Hardware maker NVIDIA has been the leading brand and also the Unmonitored Training Data has been the basis of their present Artificial Intelligence, which is used in this industry.

As per NVIDIA, they didn’t get to play with its system to properly identify faces or any other objects. The Deep Learning-based model collects information from each of its drivers and cars. This makes the use Internet-of-Things(IoT) both internal and external sensors.

7. Online Fraud Detection

Detecting cybercrimes online is one of the ways in which machine learning is shown its goal of making the internet a secure location. For example, Paypal uses ML to stop financial fraud. The business employs a set of techniques to monitor money transfers and identify which are real and which are fake.

Capital One, a financial corporation, implements machine learning to easily identify, manage, and avoid unexpected app activity. In order to immediately respond on variations in criminals’ activity, it also has used technology in support of all its anti-money financial crimes and fraud plan.

8. Dynamic Price

In modern economics, finding the right cost of a product or service has long been a difficult task. Based on the objective achieved, there are a lot of pricing structures. Everything comes with variable costs, like movie tickets, flight tickets, and cab prices. Artificial intelligence technology already makes it easier for pricing systems to observe customer behavior and create more reasonable retail prices.

One of Uber’s best important machine learning applications includes delivery charge and machine algorithms. Be prepared to pay double the price of an Uber in a busy location if you need to get there quickly for a meet and greet. When you’re visiting during the holiday period, it is really possible that flight tickets will cost double what they were before.

9. Banking Domain

In order to help avoid theft and secure accounts against hackers, banks are already deploying the most effective technology that machine learning should be able to provide. The systems can choose variables to account for while building a filter to avoid damage. Auto monitoring will exclude and protect the data from starting transactions on so many fake websites.

10. Regulating Healthcare Efficiency and Medical Services

The usage of machine learning techniques in the medical sector is currently being examined. It predicts how long patients would have to wait at different hospital departments’ emergency waiting rooms. These models have made use of key parameters that help build the technique, information about the employees at different times of the day, patient information, full records of all department conversations, or emergency department configurations. Also, disease identification, scheduling for treatments, and health condition analysis all require the application of machine learning techniques. Many of the most important machine learning applications consist of this one.

11. Video Surveillance

Some of the biggest boosts in the performance of Artificial Intelligence and machine learning may be located there. Unlike other sources, videos provide the best chance of collecting helpful information through automatic surveillance technology. Machines maintain a different perspective on objects that human brains manage, which is the only reason that really is possible.

Artificial intelligence is now a key part of modern video surveillance systems, allowing the identiﬁcation of crimes. They noticed people’s strange habits, including longer periods of inactivity accidents, seat sleeps, and many others. As a result, the technology can inform people’s attendants, helps prevents accidents. The surveillance services are increased when these actions get notified and verified as real. This is the result of machine learning executing its tasks.

There are several uses for video surveillance, including:

Stopping iron theft
Finding unusual events
protections for the structures
Operation detection
Parking structures Monitoring of traffic
shopping behaviors

12. Fraud Detection

In 2021, researchers estimate that the massive $40 billion will be lost because of online theft of credit cards. It beats the total profits of JP Morgan Chase and Coca-Cola. You should be serious about that. Another of the most important applications of machine learning is identity theft. Many payment systems, including credit/debit cards, smartphones, many accounts, UPI, and others, have resulted in an increase inside the amount of transactions. The criminal population have increased during this period, and they have become skilled at detecting areas of weakness.

Each time a user completes a payment, the machine learning system fully scans its profile in search of almost any suspicious activities. When it comes to machine learning, identification issues are mainly used to designing and delivering like fraud protection.

13. Spotify Song Recommendation System

Spotify’s music recommendation works like a mediator between creators and listeners on some kind of 2 different platform, in so many way related like TikTok’s “That Your” technique. The moment any new music was released on Spotify, a system automatically analyze every bit of metadata, including Spotify-specific and common metadata given by that of the publisher. The Spotify database stores a number of properties, including:

Song name
Released name
Author’s name
Included musicians
Author credits for songs
Production credits
Classify
Launch Date
Tags for category and subcategory
Tags: music, culture
Mood markers
format tags
First language
Instruments incorporated into the recording
Hometown or neighbourhood market

14. Online Video Streaming

In basically in every condition in which there is a chance to better or simplify any operation, like live video, machine learning is still a good potential new technology for use. Large multi-camera shows, more compact single-camera livecast, including presentations at schools and colleges, each provide several career options for machine learning applications. Machine learning can also be used in a wide variety of software applications, including live systems, video encoding applications, and video animation tools.

Below are a few machine learning techniques regarding video editing which can help technicians, presenters, and viewers by simplifying the process.

Optimized digital studios
Interactions and merging of comments
Indexing with OCR, visual elements, and translation
Smart live shifting
Dynamic picture balancing
Automatic audio improvement
Better presenter tracking,
Short videos
Reels of highlights

15. Predictions while Commuting

Traffic projected: Gps tracking services are heavily used. Our exact locations and speeds also are recorded at a remote server to traffic control while we are doing that. A traffic map is successfully created using this data. The basic issue is also that there aren’t as many vehicles with GPS, although this benefits in traffic safety and traffic problems monitoring. In all these situations, machine learning help for predicting, on a daily basis experiences, the locations that situation can be located.

Online transportation networks: This app calculates that cost of the journey when a cab has been booked. How would they reduce delay while providing these services? Machine learning is the solution. According to an interview with Jeff Schneider, technical leads for Uber ATC, they apply machine learning for predict passenger need to set cost overflow hours. ML is important towards the 24-hour period of the services.

Final Thought

All these are a few of the most popular real-world applications of machine learning. It’s time to join inside the top machine learning courses, certifications, and training with us if these applications have excited you and you’re choosing a career in machine learning.