What Is Machine Learning?
The field of data science continues to grow as more businesses, governments rapidly, and professionals are looking to harness new technology to provide better predictions. Many institutions chose machine learning as their tool of choice; the applications for this area of study are far and wide.
Machine Learning Defined
Machine learning is a branch of artificial intelligence (AI), a much larger, all-encompassing study area. AI focuses on taking problem-solving capabilities that we humans have and giving machines those same skills to solve complex problems quickly. Rather than devices following specific instructions, they can complete a task based on their understanding.
Essentially machine learning is one way to use AI. Arthur Samuel, a pioneer in this study, stated that “computers [would] the ability to learn without explicitly being programmed.” the difference with machine learning is that its primary goal is to solve problems in the physical world. Their understanding of the written text allows them to fix a problem faster than anyone could.
How Machine Learning Works
Thanks to UC Berkley, the topic of the machine learning algorithm is broken down into three main parts based. Below are the three main points:
1. A Decision Process
Machine learning primarily predicts a specific outcome or classifies massive amounts of data. Depending on the input data, which can be labeled or unlabelled (if you have a particular category, name, or class for your data), the algorithm used can analyze the data’s pattern.
2. An Error Function
An error function uses past examples to compare and understand the information. Once that’s done, it’ll work on making a prediction based on what it just received and read.
3. A Model Optimization Process
In this final part, the model optimization process continuously repeats, evaluating then optimizing the process, weighing each data point differently to find the right or most likely answer. Think of this much like when you run through different scenarios in your head, from best case to worst-case scenario, then finally, most likely case scenario; this is very similar but using machines instead.
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