2. Main Advantages of Machine Learning
Machine learning has taken the world by storm in the last decade. But why is machine learning prefarable over the conventional methods that have been used by people over a way longer period? Here are some of the most imperative benefits machine learning provides with real-life examples:
Machine learning can learn the rules to solve a problem by itself, and doesn't require long-lasting fine-tunings. For instance, in a fraud detection system, a conventional method would require manual updates to the rules as new types of fraud emerge, while an ML system would automatically adapt to new data.
Some problems are naturally very complex even for humans to understand, and a solution to such problems cannot be put into a simple and cost-effective algorithm. For instance, trying to build a self-driving car or a speech-recognition algorithm can be enourmously difficult for humans whereas, it is proven many times that machine learning problems can excel at such problems much quicker.
Machine learning is robust to fluctuating environments, and can swiftly adapt itself to new conditions unlike conventional methods that require costly updates. Conventional methods might rely on a set of assumptions, such as stationarity, that might not hold in changing weather conditions. ML algorithms, such as decision trees and neural networks, can learn from data in real-time and adapt to changing weather conditions, leading to improved robustness in weather prediction.
Conventional methods offer little chance to get insights from the solution. For instance, conventional methods might rely on hand-crafted features, such as edge detection, to recognize images. ML algorithms, such as convolutional neural networks (CNNs), can learn from large datasets of images and identify complex patterns, leading to new insights into image recognition and computer vision.
Machine learning can learn the rules to solve a problem by itself, and doesn't require long-lasting fine-tunings. For instance, in a fraud detection system, a conventional method would require manual updates to the rules as new types of fraud emerge, while an ML system would automatically adapt to new data.
Some problems are naturally very complex even for humans to understand, and a solution to such problems cannot be put into a simple and cost-effective algorithm. For instance, trying to build a self-driving car or a speech-recognition algorithm can be enourmously difficult for humans whereas, it is proven many times that machine learning problems can excel at such problems much quicker.
Machine learning is robust to fluctuating environments, and can swiftly adapt itself to new conditions unlike conventional methods that require costly updates. Conventional methods might rely on a set of assumptions, such as stationarity, that might not hold in changing weather conditions. ML algorithms, such as decision trees and neural networks, can learn from data in real-time and adapt to changing weather conditions, leading to improved robustness in weather prediction.
Conventional methods offer little chance to get insights from the solution. For instance, conventional methods might rely on hand-crafted features, such as edge detection, to recognize images. ML algorithms, such as convolutional neural networks (CNNs), can learn from large datasets of images and identify complex patterns, leading to new insights into image recognition and computer vision.
Updated on: 30/01/2023
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