Machine learning is an application of artificial intelligence (AI) that gives devices the ability to learn from their experiences and improve themselves without doing any coding. For example, when you shop from any website, it shows related searches: People who bought this also bought this.
It is a subset of artificial intelligence. It is the study of making machines more human in their behavior and decisions by giving them the ability to learn and develop their own programs. This is done with minimal human intervention, i.e. no explicit programming. The learning process is automated and refined based on the machines' experiences throughout the process.
Machines are fed good quality data and various algorithms are used to build ML models to train the machines on this data. The choice of algorithm depends on the type of data at hand and the type of operation to be automated.
Applications of Machine Learning
These algorithms help create intelligent systems that can learn from their past experiences and historical data to deliver accurate results. Many industries are applying ML solutions to their business problems or to create new and better products and services. ML is used in a variety of industries, including healthcare, defense, finance, marketing, and security.
1. Facial Recognition/Image Recognition
The most common application is facial recognition, and the simplest example of this application is the iPhone. There are many use-cases of facial recognition for security purposes such as identifying criminals, searching for missing persons, and aiding forensic investigations. Intelligent marketing, diagnosis of diseases, tracking attendance in schools, some other uses.
2. Automatic Speech Recognition
Automatic speech recognition, abbreviated as ASR, is a technology that converts spoken words into digital text. Its uses include user authentication through speech and job completion via human voice input. Speech samples and vocabulary are fed into the system to train the model. Currently ASR systems find a wide variety of applications in the following domains:
Medical assistance
Industrial Robotics
Forensic and law enforcement
Defense & Aviation
Telecommunications industry
Home Automation and Security Access Control
I.T. and consumer electronics
3. Financial services
Machine learning has several applications in financial services. Machine learning algorithms are very good at identifying fraud since they record each user's behavior and anticipate whether the attempted action is typical of that user. Financial monitoring is also a key security use case for detecting money laundering activities.
It also helps in making better business decisions with the help of algorithms that can analyze thousands of data sources simultaneously. Some other applications are credit scoring and underwriting. Virtual personal assistants like Siri and Alexa are the most common applications in our daily activities.
4. Marketing and sales
It improves lead scoring algorithms by incorporating various parameters like website visits, emails opened, downloads and clicks to score each lead. It also helps businesses improve their dynamic pricing models by using regression techniques to generate forecasts.
Sentiment analysis is another important application for predicting consumer response to a particular product or marketing initiative. Machine learning for computer vision helps brands recognize their products in online images and videos.
5. Health care
An important application is in the diagnosis of diseases and disorders that are otherwise difficult to diagnose. Radiotherapy is also improving.
Early-stage drug discovery is another key application involving technologies such as precision medicine and next-generation sequencing. Clinical trials cost a lot of time and money to complete and provide results. Applying ML based predictive analytics can improve on these factors and yield better results.
These techniques are also critical for generating prevalence estimates. Scientists around the world are using ML technology to predict the spread of epidemics.
Read More: Computer vision applications in healthcare
6. Recommender systems
Machine learning software companies use recommendation systems to effectively communicate with customers on their site. It can recommend related products, movies, web series, songs and more. Popular use-cases of recommendation systems are Amazon, Flipkart and many other e-commerce sites, along with Spotify, Netflix and other web streaming channels.
The Future of Machine Learning
The discipline will continue to progress and become more complex in the future. It is used in many areas of life and business, including healthcare, finance and manufacturing. As machine learning becomes more accessible to everyone, it becomes easier and more affordable.
We have already seen the current applications of ML, it is only said that with time and increased data and resources, the applications will improve. ML engineers are also in high demand and professionals who learn the skill are paid generously. If you are interested in building a successful ML career, now is the right time to enter the field.
The End
Experts agree that ML-based systems allow teachers to focus on other important things. The technological revolution continues to bring about rapid changes in education. For the future workforce to thrive in this new world, their adaptability and ability to innovate and persistence to learn more will be required. ML-based systems are critical tools that create opportunities to democratize access to education, personalize it, and transform teaching and learning processes.
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