Machine learning is the study of algorithms and statistical data models that computers use to perform a specific task without explicit instructions — relying on patterns and inference.
Machine learning algorithms build mathematical models based on data called “Training data” to make predictions and decisions without being programmed to perform a specific task.
There are two types of learning techniques for machine learning:
Machine learning algorithms are trained to continuously learn based on large datasets to improve future performance. These algorithms automatically update and create models that can be used in predictive modeling. This allows predictive models to be run by data scientists manually based on historical data for future events only. Predictive models are creating predictions based on probability of events and historical datasets.
In order to create a predictive model, you will need a dataset to breakdown and test to form an algorithm. Then using the algorithm and data models — you’ll continue to test with historical datasets and current datasets to form predictions and decisions. Predictive modeling makes use of data mining and probability to generate outcomes.
Below is an example of creating a predictive model:
There are many use cases for machine learning in a number of industries.
Differences in updates between Machine Learning and Predictive Modeling:
Predictive modeling is a subset and application of machine learning.
Technology that drives Machine Learning vs. Predictive Modeling:
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