Machine learning is a subfield of artificial intelligence (AI). The goal of
machine learning generally is to understand the structure of data and fit
that data into models that can be understood and utilized by people.
Although machine learning is a field within computer science, it differs
from traditional computational approaches. In traditional computing,
algorithms are sets of explicitly programmed instructions used by
computers to calculate or problem solve. Machine learning algorithms
instead allow for computers to train on data inputs and use statistical
analysis in order to output values that fall within a specific range.
Because of this, machine learning facilitates computers in building
models from sample data in order to automate decision-making
processes based on data inputs.
Any technology user today has benefitted from machine learning.
Facial recognition technology allows social media platforms to help users
tag and share photos of friends. Optical character recognition (OCR)
technology converts images of text into movable type. Recommendation
engines, powered by machine learning, suggest what movies or
television shows to watch next based on user preferences. Self-driving
cars that rely on machine learning to navigate may soon be available to
consumers.
Machine learning is a continuously developing field. Because of this,
there are some considerations to keep in mind as you work with machine
learning methodologies, or analyze the impact of machine learning
processes.
In this tutorial, we’ll look into the common machine learning methods
o f supervised and unsupervised learning, and common algorithmic
approaches in machine learning, including the k-nearest neighbor
algorithm, decision tree learning, and deep learning. We’ll explore which
programming languages are most used in machine learning, providing
y o u with some of the positive and negative attributes of each.
Additionally, we’ll discuss biases that are perpetuated by machine
learning algorithms, and consider what can be kept in mind to prevent
these biases when building algorithms.
Machine Learning Methods
In machine learning, tasks are generally classified into broad categories.
These categories are based on how learning is received or how feedback
on the learning is given to the system developed.
Supervised Learning:
In supervised learning, the computer is provided with example inputs
that are labeled with their desired outputs. The purpose of this method is
for the algorithm to be able to “learn” by comparing its actual output
with the “taught” outputs to find errors, and modify the model
accordingly. Supervised learning therefore uses patterns to predict label
values on additional unlabeled data.
For example, with supervised learning, an algorithm may be fed data
with images of sharks labeled as fish and images of oceans labeled as
water. By being trained on this data, the supervised learning algorithm
should be able to later identify unlabeled shark images as fish and
unlabeled ocean images as water
Unsupervised Learning:
In unsupervised learning, data is unlabeled, so the learning algorithm is
left to find commonalities among its input data. As unlabeled data are
more abundant than labeled data, machine learning methods that
facilitate unsupervised learning are particularly valuable.
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