A subset of artificial intelligence known as “machine learning” enables computer programs to pick up new information and adjust their behavior without the assistance of a human. It is a potent technology that is being utilized more frequently across a range of industries to handle and analyze huge data.
In a Nutshell
- Without human interaction, computer programs may learn from new data and adapt to it thanks to the field of artificial intelligence known as machine learning.
- Big data is becoming more and more available and usable because to cutting edge computational power and cloud storage.
- For a number of purposes, including commerce, marketing, financing, data centers, and fraud detection, machine learning is employed across numerous industries.
- In order for machine learning to function, a model must be built that can identify data and make predictions based on that data. In order to create patterns for its decision making process, the model uses parameters that are integrated into the algorithm.
- An asset management company using machine learning to scan the web and gather news from various businesses, industries, cities, and nations is an example of machine learning in action.
Understanding Machine Learning
Various sectors of the economy handle enormous amounts of data available in different formats and from disparate sources. The enormous amount of data, known as big data, is becoming readily available and accessible thanks to the progressive use of technology, in particular advanced computing capabilities and cloud storage.
Businesses and governments are aware of the enormous information that can be gained from leveraging big data, but lack the resources and time to track its wealth of information. As a result, different industries are employing artificial intelligence measures to collect, process, communicate and share useful information from data sets. One AI method that is increasingly being used for big data processing is machine learning.
Machine learning is the science of getting computers to learn without being explicitly programmed.
Arthur Samuel
The various machine learning data applications are formed through a complex algorithm or source code embedded in the machine or computer. This programming code creates a model that identifies data and builds predictions around the data it identifies. The model uses parameters built into the algorithm to form patterns for its decision making process. When new or additional data becomes available, the algorithm automatically adjusts the parameters to check for a pattern change, if any. However, the pattern should not change.
Uses of Machine Learning
Machine learning is used in a variety of industries for a variety of reasons. Trading systems can be calibrated to identify new investment opportunities. Marketing and e-commerce platforms can be fine tuned to provide accurate and personalized recommendations to their users based on their Internet search history or past transactions.
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Lenders can incorporate machine learning to predict bad loans and build a credit risk model. Information centers can use machine learning to cover huge amounts of news from all corners of the world. Banks can create fraud detection tools from machine learning techniques. The incorporation of machine learning in the digital age is endless as businesses and governments become more aware of the opportunities presented by big data.
Application of Machine Learning
How machine learning works can best be explained with an example from the financial world. Traditionally, stock market players such as financial researchers, analysts, asset managers and individual investors analyze a lot of information from different companies around the world to make profitable investment decisions.
However, some of the relevant information may not be widely disseminated by the media and may only be available to a select few who have the advantage of being employees of the company or residents of the country from which the information originates. In addition, humans can only collect and process a limited amount of information in a given time frame. This is where machine learning comes in.
An asset management firm can employ machine learning in its investment research and analysis area. Let’s say the asset manager only invests in mining stocks. The model embedded in the system scans the web and collects all kinds of news from companies, industries, cities and countries, and this collected information constitutes the data set. Asset managers and company researchers would not have been able to get the information from the dataset using their human faculties and intellects.
The parameters built in conjunction with the model extract from the dataset only data on mining companies, exploration industry regulatory policies and political events in specific countries.
Example of Machine Learning
Suppose mining company XYZ has just discovered a diamond mine in a small town in South Africa. A machine learning tool in the hands of an asset manager specializing in mining companies would highlight this data as relevant. The machine learning tool’s model would then use an analytics tool called predictive analytics to make predictions about whether the mining industry will be profitable over a period of time, or which mining stocks are likely to increase in value at any given time, based on the newly discovered information, without any input from the asset manager.
This information is passed on to the asset manager to analyze and make a decision for his or her portfolio. The asset manager can then make a decision to invest millions of dollars in stock XYZ.When faced with an unfavorable event, such as the South African miners’ strike, the computer algorithm automatically adjusts its parameters to create a new pattern. In this way, the machine’s built in computational model is kept up to date even with changes in world events and without the need for a human to tweak its code to reflect the changes. Because the asset manager has received this new data in time, it can limit its losses by going out of value.
Wrap Up
In summary, machine learning is an effective tool that helps businesses and governments manage and evaluate massive amounts of data. It is increasingly used for a range of purposes, including business, marketing, lending, information hubs, and fraud detection, in a number of different industries. Machine learning enables software to learn and adapt to new data without the need for human interaction by developing a model that recognizes data and generates predictions around the data it identifies.
FAQs
A branch of artificial intelligence called “machine learning” enables computer systems to pick up new information and adjust to it without the help of a human.
Machine learning is used in many different industries for a variety of tasks, including discovering new investment opportunities in trading systems, offering individualized recommendations in marketing and e-commerce platforms, forecasting defaults in the credit industry, reporting news in information centers, and spotting fraud in banks.
A model that recognizes data and derives predictions from it is created using a sophisticated algorithm or source code that is embedded in a computer. The model creates patterns for your decision making process using parameters included in the algorithm. The program automatically modifies the settings in response to new data to check for pattern changes.
To make wise investment choices, an asset management firm can utilize machine learning to search the web and acquire news from businesses, industries, cities, and nations. For instance, if a mining firm finds a diamond mine in South Africa, a machine learning tool can use predictive analytics to forecast the profitability of the mining sector or which mining stocks are most likely to see an increase in value based on the newly discovered data.
A miner’s strike, for example, would be an undesirable event that would cause the computer system to automatically change its parameters and build a new model. This eliminates the need for a human to modify the machine’s programming to reflect changes in the outside world and keeps the machine’s internal computational model current.
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- IBM – What is Machine Learning?
- TechTarget – Machine Learning definition
- MIT Sloan School of Management – Machine learning, explained
