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...machine learning is a class of methods for automatically creating models from data. Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, Regression, etc.) depends on the kind of problem you’re solving, the computing resources available, and the nature of the data. Ordinary programming algorithms tell the computer what to do in a straightforward way. For example, sorting algorithms turn unordered data into data ordered by some criteria, often the numeric or alphabetical order of one or more fields in the data. Machine learning algorithms explained | Martin Heller - InfoWorld

  • Linear Regression algorithms fit a straight line, or another function that is linear in its parameters such as a polynomial, to numeric data, typically by performing matrix inversions to minimize the squared error between the line and the data. Squared error is used as the metric because you don’t care whether the Regression line is above or below the data points; you only care about the distance between the line and the points.
  • Nonlinear Regression algorithms, which fit curves that are not linear in their parameters to data, are a little more complicated, because, unlike linear Regression problems, they can’t be solved with a deterministic method. Instead, the nonlinear Regression algorithms implement some kind of iterative minimization process, often some variation on the method of steepest descent.



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