Python机器学习项目

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.
Two of the most widely adopted machine learning methods are
supervised learning which trains algorithms based on example input and
output data that is labeled by humans, and unsupervised learning which
provides the algorithm with no labeled data in order to allow it to find
structure within its input data. Let’s explore these methods in more
detail.
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.
A common use case of supervised learning is to use historical data to
predict statistically likely future events. It may use historical stock
market information to anticipate upcoming fluctuations, or be employed
to filter out spam emails. In supervised learning, tagged photos of dogs
can be used as input data to classify untagged photos of dogs.
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.
The goal of unsupervised learning may be as straightforward as
discovering hidden patterns within a dataset, but it may also have a goal
of feature learning, which allows the computational machine to
automatically discover the representations that are needed to classify raw
data.
Unsupervised learning is commonly used for transactional data. You
may have a large dataset of customers and their purchases, but as a
human you will likely not be able to make sense of what similar
attributes can be drawn from customer profiles and their types of
purchases. With this data fed into an unsupervised learning algorithm, it
may be determined that women of a certain age range who buy
unscented soaps are likely to be pregnant, and therefore a marketing
campaign related to pregnancy and baby products can be targeted to this
audience in order to increase their number of purchases.
Without being told a “correct” answer, unsupervised learning methods
can look at complex data that is more expansive and seemingly unrelated
in order to organize it in potentially meaningful ways. Unsupervised
learning is often used for anomaly detection including for fraudulent
credit card purchases, and recommender systems that recommend what
products to buy next. In unsupervised learning, untagged photos of dogs
can be used as input data for the algorithm to find likenesses and classify
dog photos together.