Machine Learning is a subfield of computer science which gives computers the ability to learn without being explicitly programmed. It is concerned with construction of algorithms than can learn and make predictions from data.
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Machine Learning is changing the way we do things, and it has started becoming main-stream very quickly. While many factors have contributed to this increase in machine learning, one reason is that it is becoming easier for developers to apply it. And, that is through open source frameworks. Yet most would agree that these days the largest fraction of machine learning researchers come from computer science.
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For any tech enthusiast, knowing certain Machine Learning Algorithms and its applications have now become very important. Tech giants like Google, Amazon, Facebook, Walmart are using Machine Learning significantly to keep their business tight enough to compete with their rivalries.
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The most important thing you must know if you want to get succeed as a Machine Learning engineer is how you should deal with the most precious thing called "DATA". Data analysis is the most important thing that you need to master in order to proceed with Machine learning. Although it may sound surprising, unless you are able to analyze the data correctly, you cannot build a model to use on the data. Now Data analysis is a pretty big field in itself and to work on data analysis.
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Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.
Source: HOB7062
Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.
Source: HOB3330
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