Machine Learning Overview
Both machine learning as well as Artificial Intelligence (AI) are not novelty innovations. The concept of machine learning has already been defined from as early as 1959 by Arthur Samuel. Machine learning can simply be defined as the ability of a machine, computers, to learn to functions in various ways that they were not specifically programmed to do. Machine learning is applied in various cases where the desired outcome is known. It is also applied where data is not known before hand or when and where the learning is as result of interactions between the model and the environment.
Why Machine Learning is so Valuable
Machine Learning is especially valuable to those who know what they want but do not know the imperative input variables in order to make the decisions. Thus, machine learning algorithms are provided with the goal, or goals, and it then learns by using data to determine important factors in achieving the goal.
The Internet of Things and Machine Learning
Machine learning has gained a substantial boost in being applied to the Internet of Things (IoT), especially in recent years. It can even be said that machine learning is being used to greatly enhance IoT in several ways.
1.  Cost Savings in Industrial Applications
Where the industrial sector is concerned, predictive capabilities can only be an advantage. With machine learning drawing data from a variety of sensors either in or on machines, machine learning algorithms can make use of the data to learn typical operations and functions of the machine and know when there are abnormalities. You might also be interested in an Ultimate Guide to Big Data.
2.  Shaping Individual Experiences
Machine learning forms part of daily lives without many people realizing it. Social media platforms as well as streaming services make use of machine learning. They have also started applying it to IoT to enhance personal and individual experiences. Algorithms are used to detect people’s preferences and the platforms then adequately cater for content according to the patterns and trends that are identified.
3.  Applications in healthcare
In the healthcare industry, IoT is often referred to as the Internet of Medical Things, or IoMT. Healthcare facilities that make use of interconnected devices such as medical devices, patient monitoring tools, wearables, and numerous others, can make use of machine learning to enhance IoMT greatly. This can be done through the great amounts of data generated by these devices being used by machine learning to generate actionable insights for an array of diseases. Through this, chronic disease management and acute patient care needs can be improved greatly.
4.  Machine Learning, IoT and agriculture
Machine learning is also being used to enhance IoT used in agricultural activities. Both IoT and remote sensing have aided in the development of new technologies. By applying machine learning to such devices, a lot of time and costs can be saved in agricultural activities, especially on a grand scale where actions applied to individual devices is possible, but tedious and involves a great amount of effort which can slow productivity. For more information visit OQLIS’ website here: https://www.oqlis.com/.
