What Is Machine Learning (ML)?
Machine Learning is designed to help computers learn in ways similar to how the human brain learns.
ML uses large data sets and algorithms (models) to analyze and categorize data or make predictions.
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Models can improve on their own and can be updated by humans.
Unlike similar technologies like Deep Learning, Machine Learning doesn’t useneural networks.
Machine Learning has existed in various forms since the 1960s and is increasingly widely used.
Around 70% of financial services companies use some form of ML by some measures.
Machine Learning Definition
Machine Learning starts with two elements: analgorithmand a data set.
The data set may or may not beclassifiedor labeled to assist the algorithm.
The algorithm then processes the data to produce an output.
The more data the algorithm processes, the more accurate it should become.
This is what is meant by “learning.”
Humans learn basic concepts or skills and then improve through repetition and extrapolation.
That’s the goal of ML, too.
With ML, the model is designed to change itself based on experience with more data and tasks.
For example, an image detection algorithm might analyze pictures containing a person with red hair.
Cross-validation is a method of testing a machine-learning model; developers usually use it to combat overfitting.
One version of cross-validation involves splitting the original data set into smaller chunks.
Some are held back, while the others run through the model.
You then compare the “control” groups with the “test” groups to how the algorithm performs.
