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Machine Learning: Efficient IoT Analysis
As a result of the rapid growth in the hardware, software and communication technologies, a network of connected devices has emerged. This is the Internet of Things (IoT). IoT devices generate a massive amount of data and machine learning enables a more straightforward analysis of that data for improving the efficiency of customer service and also for reducing the costs and energy.
Machine learning is an integral part of AI that aims to replicate the process of learning the way a human mind does. Machine learning considers the human brain as a powerful computer that combines some external signals and then integrates those signals and giving as outputs. The most prominent advantage of the AI and machine learning is the automated big data analysis. Compared to the analysis by human professionals, machine learning makes the outcomes more accurate and ensures lesser time-consuming and efficient data analysis.
Through the analysis of the huge amount of data or by understanding regular patterns of the algorithm, machine learning can predict or forecast the desired events which are to occur in the future. Predictive analytics helps organizations to estimate future demand, growth, profit, and status of the organization. The system can quickly detect abnormalities and inconsistencies in the data and provide information about it, enabling organizations to take actions accordingly.
The machine learning system powered by AI, cannot only perform predictive analytics but also has the prescriptive power that provides future predictions with the determination of the factors and parameters should be changed to get the desired outcome. A machine learning system additionally helps organizations to find long-term future trends and process, recognize, select, sort, and associate the big data collected for making predictions.
However, there is still a long path to go for machine learning technologies, as it still works with human touch and guidance. For making these systems much efficient and effective, continuous supervision and rectifications are needed, especially for analyzing the big data collected by IoT.
For making the machine learning technology more fruitful, it is imperative to combine human touch with the self-learning systems. Effective management of the machine learning system is the only way to get dynamic IoT analysis for an excellent future for AI and ML.