Top 10 Applications of Machine Learning

Machine learning is a demanding career path for several reasons. Firstly, the demand for machine learning professionals is increasing rapidly as more industries leverage the applications of machine learning and artificial intelligence, which all comes under data science. Additionally, the shortage of skills required by businesses in the AI field, particularly in the IT industry, further emphasizes the increasing demand for machine learning experts. Overall, the demand for machine learning professionals is fueled by the rapid growth of AI applications across industries, making it a lucrative and promising career choice for individuals interested in this field.

According to a 2019 report by Indeed, the Machine Learning Engineer is the number one job in salary, posting growth, and general demand.

According to Statista:

· The market size in the Machine Learning market is projected to reach US$79.29bn in 2024.

· The machine learning market is poised for exponential growth, with an expected annual growth rate (CAGR 2024-2030) of 36.08%. This projection could result in a staggering market volume of US$503.40bn by 2030, indicating the long-term viability and potential of a career in machine learning.

· In global comparison, the largest market size will be in the United States (US$21.14bn in 2024).

What is Machine Learning?

Machine learning is a subfield of artificial intelligence (AI) that focuses on enabling machines to learn and improve from data analysis without explicit programming. It is a discipline that allows machines to learn, grow, develop, and adapt to new data. Machine learning algorithms are molded on a training dataset to create a model, which is then used to predict new input data. The performance of ML algorithms adaptively improves with an increase in available samples during the 'learning' process.

Machine learning has numerous applications across various industries, including computational finance, computer vision, computational biology, automotive, aerospace, manufacturing, and natural language processing. It is a promising field with significant potential to derive business value from the available data.

Now, we shall discuss the applications of machine learning.

1. Social Media Features

All social media platforms utilize machine learning and its algorithms to create valuable features for their organizations' growth.

For example:

Facebook—As we know, Facebook is a worldwide social media platform. We are not aware of how it records and monitors user activities. All activities include chatting, the time we spend, comments, likes, posts, updates, everything. Machine learning learns user activities and their likes and dislikes and pops up friends and page suggestions for your profile.

2. Product Recommendations

Product recommendation is one of the popular applications of machine learning. It is a unique feature utilized by almost every organization, particularly e-commerce sites, in the booming digital marketing field.

This product recommendation is an advanced application of ML. We can monitor and track our website behavior based on previous purchases, cart history, and search patterns using this advanced machine learning and artificial intelligence application technique.

3. Image Recognition

Image recognition is one of the popular applications of machine learning. Machine learning is used in image recognition to train models that can automatically identify and classify objects within images. One common approach is convolutional neural networks (CNNs), specifically designed to work with image data. These models learn to extract features from images and make predictions based on those features.

For example, in a facial recognition system, a CNN can be trained on a dataset of images with labeled faces to learn to distinguish between different individuals. As the model is trained on more data, it becomes better at recognizing patterns and making accurate predictions, enabling it to identify faces in new images with high accuracy.

By training the model on a large dataset of labeled MRI images, it can learn to recognize patterns associated with different types of abnormalities. Once trained, the model can be used to automatically analyze new MRI scans and flag any areas of concern for further review by medical professionals. This image recognition application speeds up the diagnostic process and helps in early detection of health issues.

4. Sentiment Analysis

Sentiment analysis is a machine learning technique used to analyze and classify the emotional tone of text data as positive, negative, or neutral. It is also known as opinion mining or emotion artificial intelligence. Sentiment analysis models use natural language processing (NLP) and machine learning to determine the overall sentiment conveyed by a particular text, phrase, or word, expressed as a numerical rating known as a "sentiment score."

For example:

Sentiment analysis is used in various applications, such as customer feedback analysis, brand monitoring, market research, and social media sentiment tracking, providing valuable insights for businesses to make data-driven decisions and understand customer perceptions effectively. Sentiment analysis can be applied to everything from brand monitoring to market research and HR, helping companies to glean deeper insights, become more competitive, and better understand their customers.

5. Automating Employee Access Control

Automating Employee Access Control is a top application of machine learning used by organizations to determine the level of access employees need based on their job profiles. Machine learning algorithms analyze and classify employees based on their roles, responsibilities, and job requirements and then automatically grant or revoke access to specific systems, applications, or data.

For example:

Amazon launched a machine learning contest on Kaggle to develop an automated employee access control system to predict which employees should be granted access to restricted areas based on their job profiles and behavior patterns. Other organizations, such as Key Dynamics Solutions and Bytecode, offer machine learning-based access control solutions to automate granting and revoking employee access.

6. Regulating Healthcare Efficiency and Medical Services

Many healthcare sectors are actively implementing machine learning algorithms for better organization management. These algorithms can predict patient waiting across various departments in the hospital.

Machine learning is used in healthcare to draw insights from large medical data sets, improve patient outcomes, automate healthcare professionals' daily workflows, accelerate medical research, and increase operational efficiency. It is used in various applications such as disease outbreak prediction, drug discovery, hospital management optimization, and the development of better diagnostic tools to analyze medical images.

Machine learning can improve diagnosis, develop new treatments, reduce costs, enhance data security, and improve patient care. It can also help automate medical billing, clinical decision support, and the development of clinical practice guidelines within health systems.

For example:

Johns Hopkins Hospital, Cleveland Clinic, and UCLA Medical Center are among the top hospitals in the United States using machine learning.

7. Self-driving cars

One of the best machine learning applications is self-driving cars.

For example:

Companies like Tesla, Waymo, and Uber use machine learning in their self-driving car technologies, vehicle design, and supply chain management.

8. Email Spam and Malware Filtering

Whenever we get a new email, it's sorted out as important, normal, or spam. The important ones appear in our inbox with a special symbol, while the spammy ones go straight to the spam folder. This whole system works due to Machine Learning.

Below are some spam filters used by Gmail

· Blacklist filtering

· Content filtering

· Link reputation filtering

· Machine learning

· Sender reputation

· Recipient behavior

Some machine learning algorithms, such as:

Multi-Layer Perceptron

Decision tree

Naïve Bayes classifier

These are the most crucial machine-learning algorithms for email spam filtering and malware detection.

9. Virtual Personal Assistant:

Machine learning enables Virtual Personal Assistants to understand user queries, personalize responses, and improve accuracy through continual learning from user interactions. Natural Language Processing (NLP) algorithms process spoken or typed commands, while data analysis drives adaptation to individual preferences, optimizing performance and usability.

Many products incorporate virtual personal assistants; here are a few examples:

· Smartphones: Most modern smartphones have built-in virtual assistants, such as Apple's Siri, Google Assistant, or Samsung's Bixby.

· Smart speakers: Smart speakers like Amazon Echo (with Alexa), Google Home (with Google Assistant), and Apple HomePod (with Siri) are voice-activated devices.

· Smart displays: Smart displays are smart speakers with screens.

· Smartwatches: Some smartwatches, such as the Apple and Samsung Galaxy Watch, also have built-in virtual assistants.

10. Banking Domain

Nowadays, encrypting banking details has become a common issue facing any Bank in any corner of the world. To secure banking details or customer data from malware hackers, banks use machine learning applications to protect them from fraud.

The algorithms ascertain the relevant factors for establishing a filter to mitigate harm. Unauthorized websites will be automatically filtered and prohibited from engaging in transactions.

For example:

These Machine learning applications are commonly used for fraud detection in banking.

· Logistic regression

· Decision trees

· Random forest

· Support vector machines (SVMs)

11. Language Translation

Machine learning is a translator's secret weapon for VPAs. It analyzes these pairs, learning the connections between words and phrases across languages. Over time, it can use this knowledge to translate new sentences on its own, even accounting for slang or unusual phrasing—just like a seasoned translator getting better with experience!

For example:

Here are some products that leverage machine learning for language translation:

· Google Translate

· Microsoft Translator

· DeepL

· Waygo

· Skype

There are two primary machine-learning applications used for language translation:

1. Statistical Machine Translation (SMT)

2. Neural Machine Translation (NMT)

Moreover, various machine learning applications are springing into our everyday lives, and new technologies have emerged due to the implementation of machine learning and Artificial intelligence in every sector, from the Government to the IT sectors.

In conclusion, machine learning is a cornerstone of modern innovation, offering solutions to complex problems and unlocking new possibilities across diverse fields. Its capacity to derive insights from data, adapt to changing circumstances, and augment human capabilities underscores its significance in shaping the digital landscape. As we harness its potential responsibly and ethically, machine learning will undoubtedly remain at the forefront of driving progress, fostering creativity, and improving the quality of life for individuals and society.