Commonly, machine learning applications are used in daily life in different cases. Here, we understand how machine learning applications change industries in daily life. Machine learning is a rapidly growing technology in artificial intelligence.
What is Machine Learning?
Machine learning is a set of techniques and tools that help computers learn and work according to themselves. Machine learning is a part of artificial intelligence and computer science, which work on algorithms and initiate human learning to improve accuracy. Machine learning works on the basis of these parts:
- Decision process: A machine learning algorithm is used to predict raw data and provide real data.
- Error function: It evaluates the predictions of models.
- Optimization process of the model: In the training set, if the model can fit the data points, it reduces the discrepancy.
Many e-commerce companies are using machine learning algorithms along with other IT security tools to prevent fraud and improve recommendation engine performance.
To know more about Machine Learning and Deep Learning
Let’s take a look at other real-world machine learning applications that are taking place in the real world.
Most popular Machine Learning Applications and Examples
1. Social Media Features
Social media platforms use machine learning algorithms and approaches to create some engaging and great features. For example, Facebook recognizes and records your activity, chats, likes, comments, and time spent on certain types of posts. Machine learning learns from your own experiences and suggests friends and Pages on your profile.
2. Product Recommendations
Product recommendations are one of the most popular and well-known applications of machine learning. Product recommendations are one of the key features of almost all e-commerce websites today and are an advanced application of machine learning techniques. The website uses machine learning and AI to track user behavior and recommend products based on past purchases, search patterns, and cart history.
3. Sentiment analysis
Sentiment analysis is one of the most essential applications of machine learning. Sentiment analysis is a real-time machine learning application that determines the emotions and opinions of speakers and writers. For example, if someone writes a review or email (or any other form of document), the sentiment analyzer immediately detects the real idea and tone of the text. This sentiment analysis application can be used to analyze review-based websites, decision-making applications, and more.
3. Image Recognition
Image recognition is an approach to cataloging and detecting features and objects in digital images and is one of the most important and notable machine learning and AI techniques. This technology is used for further analysis, recognition, and facial recognition.
5. Predict Traffic Patterns
To illustrate this, consider the example of Google Maps. When you enter your location on the map, the application collects large amounts of data about current traffic, generates predictions about future traffic, and identifies the fastest route to your destination.
6. Self-Driving Cars
Self-driving cars use unsupervised learning algorithms that rely heavily on machine learning techniques. This algorithm allows the vehicle to collect information about its surroundings from cameras and sensors, understand it, and choose the actions to take.
7. Catching Malware
The process of detecting malware using machine learning (ML) consists of two basic steps. First, it analyzes suspicious activity within the Android environment and prepares a collection of appropriate features. The second is to train the system to use machine and deep learning (DL) techniques on the generated features to detect future cyber attacks in such environments.
What is malware? History, Definition, 7 main types and real-world examples
8. Virtual Personal Assistant
Virtual personal assistants allow people to access relevant information through text or voice. When a question is entered into the system, the personal assistant collects information by searching for that question or remembering similar questions the person has asked in the past. Common ML technologies related to virtual assistants include speech recognition, speech-to-text, natural language processing, and text-to-speech.
9. Online Fraud Recognition
One of the most important applications of machine learning is fraud detection. Whenever a customer completes a transaction, machine learning models carefully examine the customer’s profile, looking for unusual patterns to detect online fraud.
10. Stock Market and Day Trading
When it comes to stock markets and day trading, machine learning uses algorithmic trading to extract important data and automate or support important investment activities. Some of the tasks involved in successful portfolio management and choosing when to buy and sell stocks are accomplished using ML.
Real-world examples of machine learning application
With an understanding of the common machine learning uses, let’s explore some examples of the popular applications in the market that rely heavily on machine learning.
1. Social Media, Example-Facebook
Automated friend tagging suggestions on Facebook are one of the best machine learning applications. Facebook uses face detection and image recognition to automatically identify similar faces in its database and recommends you tag the person using Deep Face (a project of Facebook’s deep learning division).
2. Transportation, Example- Uber
Uber is a customized taxi application that uses machine learning to automatically find passengers and provide them with options to take them to home, work or other common locations based on their history and patterns. Additionally, the app uses ML algorithms to accurately predict the estimated time of arrival (ETA) at a specific destination by analyzing traffic conditions.
3. Language translation (Google Translate)
To overcome all language barriers and make traveling to foreign countries easier, Google Translate uses Google Neural Machine Translation, which relies on Natural Language Processing (NLP) to translate words in thousands of languages and GNMT . It also uses POS tagging, named entity recognition (NER), and chunking to maintain the word tone.
What is natural language processing? Definition, advantages and examples
Machine Learning Application in Healthcare
To understand what machine learning applications are in healthcare, let’s take a look at the following uses and examples:.
1. Individual treatment/medication
Machine learning is important to identify genetic markers and types of genes that respond to specific treatments and drugs. By enabling better disease assessment, machine learning, combined with advanced analytics, enables personalized medicine and treatment based on individual health information. This helps reduce overall health care costs.
2. Genetics and Genomics
Machine learning in genomics influences many fields, such as genetic research, genetic modification, and genome sequencing, to identify the fundamental impact that genetics has on human health. Additionally, other emerging applications include predictive testing for early disease diagnosis to improve the quality of patient care.
3. Healthcare for personalized treatment
A common and serious problem for the medical industry is when a potential drug is effective in some patients but causes serious side effects in others. To solve this problem, Genentech collaborated with GNS Healthcare to implement an innovative solution using biomedical data. Genentech used AI and ML with GNS reverse engineering and forward simulation techniques to identify gene-based patient response markers that could lead to targeted treatments.
4. Cancer prognosis and prediction
ML algorithms have been applied in cancer research because they can identify important features within complex datasets. It is used to build predictive models using techniques such as artificial neural networks (ANN), Bayesian networks (BN), and decision trees (DT). This helps in accurate decision-making and modeling of malignant disease progression and treatment.
5. Drug discovery and manufacturing
To manufacture or discover a new drug, multiple tests must be conducted on millions of compounds, which is costly and time-consuming. This time-consuming, multi-step drug discovery process can be accelerated using ML.
6. Drug discovery
One of the most important applications of IBM Watson is drug development. Pfizer leverages IBM Watson’s machine learning capabilities in immuno-oncology research to filter data and select the best participants for clinical trials, facilitating faster drug discovery.
Machine Learning Applications in Finance
Are you wondering what are the machine learning applications in finance? Check out the list of machine learning applications in banking and finance industry for a deeper understanding.
1. Target Account Holder
How do banks know who their most valuable customers are? Machine learning algorithms built into customer segmentation software can help identify account holders with large balances or debt. This allows banks to more accurately identify customer business trends and identify the most influential Preferred Bank customers.
2. Fraud detection
Fraud prevention is one of the most important applications of machine learning in the banking and financial industry. This technique has been applied to searching large amounts of transaction data and identifying patterns of unusual behavior. Every purchase a customer makes is evaluated in real time, and the fraud score indicates the likelihood that the transaction is fraudulent. If a fraudulent transaction occurs, the transaction will be blocked or frozen and subject to manual inspection. This entire process takes just a few seconds.
3. Prediction of Loan Eligibility
The foundation of the banking industry is loans and the interest generated from them. Therefore, proper verification is necessary before choosing who should be allowed to give loans and who should not. For this purpose, banks apply classification algorithms such as logistic regression, Naive Bayes classifiers, and random forests to classify customers into “churners” or “non-churners.”.
Applications of Machine Learning in Real-World
Machine learning is a modern innovation that has many real-world applications in various industries and daily life. Whether getting work done (using Google Maps to find a route with less traffic), shopping online (at Amazon or Walmart), or communicating with friends online (Meta), this technology can come to us in stages. It is only a matter of time before we see more practical machine learning applications unlocking new technological advancements.
So, if you are interested in gaining in-demand machine learning skills, look no further. Explore a wide range of Machine Learning and Artificial Intelligence courses from world-class universities in partnership with Emeritus and gain practical knowledge and experience working on real-world ML projects. Let’s enjoy learning!
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