In the process of transformation of Information, the ultimate goal is to derive Actionable Intelligence.
In the current and near future, with the advent of digitization, an enormous influx of data/information is being generated and due to that a major tectonic shift is created across the organizations. Hence, it would be a herculean task for the Organizations to handle Big Data.
All these challenges can be addressed with the usage of sophisticated Machine Learning Algorithms. Capabilities of Machine Learning extends to keep the consistency and reliability of organizational data, which in-turn results in building the prediction models to contain business problems.
Machine Learning deals with rule based solutions, and they are based on so many factors which are tuned to resolve the difficulty in finding out the accuracy of human mind.
The term Machine Learning is defined by Arthur Samuel in 1959.
Few other challenges of Machine learning:
Solutions to the Machine Learning Challenges:
Machine Learning has wide variety of solution techniques. Such as,
World is at the cusp of Automation and Innovation. With increase in data, the processes and dependencies surrounding it have increased. Automation is the need of the hour and Artificial Intelligence defines intelligence as the ability to acquire, understand and apply knowledge, or the ability to exercise thought and reason. AI is the computational study which makes it possible to perceive reason and act.
Challenges of Artificial Intelligence:
Solutions of the Artificial Intelligence:
It constitutes the AI landscape, meaning that have been designed, trained, and optimized by human engineers to achieve the goals. Artificial Neurons simulate aspects of the intelligence of studying the goals and best accurate the predictions based on the rules. Rules are generation of the human fooling or new generation of the black-box techniques used in identification. Avoiding the negative impact of the sources can improve the performance on the decisions. Commonsense reasoning problems to solve the solution of the cause-effect relations, casual reasoning together with system’s other modeling and reasoning capabilities to consider situations will minimize the challenges.
supervised learning task of inferring a function from labeled of training data.
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