What is Machine Learning?


Machine Learning (ML) is an application of artificial intelligent (AI) technology that enables software to become much more accurate at predicting future results without being directly programmed to do this. Machine Learning Algorithms rely on past data as inputs to predict future outcomes. This provides significant advantages over traditional forecasting methods, in many cases. It also reduces the time required for businesses to decide and implement decisions, leading to higher levels of productivity and efficiency.

Machine Learning

 

Businesses have identified Machine Learning (ML) opportunities in a number of areas. In areas such as supply chain management, operational management, human resources, consumer behavior, and e-commerce, the accuracy and precision of forecast models has enabled increased productivity and operational effectiveness. In all cases, the key enabler has been a powerful, structured software application. One of the most common machine learning algorithms is the decision tree. In this case, a data-driven, recurrent tree is used to generate and track relevant information in order to provide an accurate forecast of future results.

 

In earlier years, Machine Learning was limited to numerical analysis. Starting in the 1970s, advances in programming languages like C++ opened the door to machine programming. With greater availability of memory, processors, and computer networks, machine learning became possible. Today, machine learning is commonly applied to a wide range of domains, from military applications to manufacturing, in order to aid operations in all industries.

 

In machine learning, a series of algorithms are applied to inputs to generate a predicted outcome. For example, in medical billing, an algorithm is used to infer a billable patient's eligibility for Medicare. Medical billing machines then make assumptions about the inputs, and the resulting model can be used to generate a medical bill. This process is called supervised learning, and a large number of supervised learning models have been developed over the past few decades.

 

Although machine learning algorithms have been around for decades, the discipline emerged with the development of supervised learning. These algorithms are typically more directed than traditional reinforcement based approaches. Reinforcement learning methods, in which the output of an algorithm is influenced by the actions taken in the input environment, is much more closely tied to the physical behaviors of the system. As such, reinforcement learning provides an easy platform for training a wide variety of real-life situations, where the goal is to maximize the expected value of a given input.

 

A Machine Learning Algorithm is typically defined as a function that minimizes a cost function over an extended range of inputs. Typically, the function will also contain interactions that help the learner build an educated understanding of the data set. Machine Learning involves the implementation of supervised learning methods in real world settings. In a manufacturing setting for example, machine learning algorithms can be used to optimize the design of production machines. Optimizing the production process calls for the identification of the parameters that minimize the overall productivity output. Once the parameters are optimized, the software is tested on real data sets to ensure that the desired results are obtained.

 

The machine learning algorithms being used today in industries such as entertainment, finance and stock broking are often used alongside with current knowledge to make better predictions about what future market behavior will reveal. Some Machine Learning applications have even been used to predict the outcome of real-world sporting events. Current predictions are made using historical data and past performance. Many of these applications are able to filter out the non-linearly predictive elements. In other words, machine learning models may not necessarily be 100 percent accurate but the accuracy of predictions tend to be very solid and reliable. While some industries are currently experimenting with applying Machine Learning to their businesses in the finance and insurance industries, many more industries that rely on complex financial models rely on supervised learning models.

 

While it's still early days for machine learning algorithms, once they are established, they will revolutionize how businesses operate. Algorithms such as supervised learning algorithms and regression or decision trees, which both rely on a known statistical data set, are currently being used to support financial services, transportation and consumer protection agencies, among others. Businesses that have not yet begun exploring the potential uses of machine learning will be faced with a difficult decision-making phase in the near future. Business owners and managers will need to consider their bottom line, their human resources budget and competitive threats as well as their strategic planning and long term goals. If an investment is made in supervised learning algorithms, the benefits could last for decades, if not generations.


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