Machine Learning: 2 Books in 1: Machine Learning for Beginners, Machine Learning Mathematics. An Introduction Guide to Understand Data Science Through the Business Application

Types of Machine Learning
 
Supervised Machine Learning
The "supervised machine learning" is widely used in predictive big data
analysis because they can assess and apply the lessons learned from
previous iterations and interactions to new data set. These learning
algorithms are capable of labeling all their current events based on the
instructions provided to efficiently forecast and predict future events. For
example, the machine can be programmed to label its data points as "R"
(Run), "N" (Negative) or "P" (Positive). The machine-learning algorithm
then labels the input data as programmed and gets the correct output data.
The algorithm compares the production of its own with the "expected or
correct" output, identifies potential modifications, and resolves errors to
make the model more accurate and smarter. By employing methods like
"regression", '' prediction", ''classification", and "boosting of ingredients" to
properly train the learning algorithms, any new input data can be fed to the
machine as "target" data set to assemble the learning program as desired.
This jump-starts the analysis and propels the learning algorithms to create
an "inferred feature", which can be used to generate forecasts and
predictions based on output values for future events. Financial organizations
and banks, for example, depend heavily on machine-learning algorithms to
track credit card fraud and foresee the likelihood of a potential customer not
making their loan payments on time.
Unsupervised Machine Learning

---

Companies often find themselves in a situation in which data sources are
required to generate a labeled and categorized training data set are
unavailable. In these conditions, the use of unsupervised machine learning
is ideal. “Unsupervised learning algorithms” are commonly used to describe
how the machine can produce "inferred features" to illustrate hidden
patterns from an unlabeled and unclassified component in the stack of data.
These algorithms can explore the data so that a structure can be defined
within the data mass. Although the unsupervised machine learning
algorithms are as effective as the supervised learning algorithms in the
exploration of input data and drawing insights from it, the unsupervised
algorithms are not capable of identifying the correct output. These
algorithms can be used to define data outliers; to produce tailor-made
product suggestions; to classify text topics using techniques such as "self-
organizing maps”, "singular value decomposition" and "k-means
clustering". Customer identification, for example, customers can be
segmented into groups with shared shopping attributes and targeted
with similar marketing strategies and campaigns. Consequently,
unsupervised learning algorithms are very common in the online marketing
industry.