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

The Trade-Off between Bias and Variance
In the context of statistical learning, “bias” and “variance” are inversely
related. For a model exhibiting high bias, the variance score will be reduced
significantly and vice versa. There is a compromise that needs to be
made between these two factors, which drives the selection of
the model and its configuration to resolve the targeted issue by achieving a
fine balance between the two. The right level of flexibility is critical to the
efficiency and performance of any statistical learning technique in both the
"regression" and "classification" environments. The trade-off between
"bias" and "variance" of the model and the subsequent "U-shape" in the test
error poses a major challenge.

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Chapter 2: Machine Learning
Algorithms
Machines are now able to learn from and train on their own by using
previous computations and underlying algorithms to produce high-quality,
easily reproducible decisions and results. Machine learning has been around
for a long time now, but the recent developments in machine learning
algorithms have made it possible for machines to process and analyze large
volumes of data efficiently. This is accomplished by using high speed and
frequency automation to apply advanced and complex mathematical
calculations to the machines. The sophisticated computing machines of
today can rapidly evaluate the ginormous volumes of data and produce
faster and more accurate results. Companies that use machine learning
algorithms have improved flexibility to adapt the training data set to meet
their business requirements and train the machines accordingly. These
tailor-made machine learning algorithms allow businesses to identify
potential hazards and growth opportunities. Typically, in collaboration with
artificial intelligence technology and cognitive technologies, machine
learning algorithms are used to produce computers that are highly effective
and extremely efficient in processing huge amounts of information or big
data and to produce highly accurate results.
Hundreds and thousands of machine learning algorithms have already been
generated as this research field continues to proliferate. Here are some of
the most commonly used algorithms categorized on the basis of its type of
machine learning:

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To refresh your memory, “supervised learning” is driven by the data
scientists who provide guidance for teaching the algorithm of what
conclusions it must make, using predefined training data set. “Supervised
learning” requires information on all possible outputs of the algorithm and
the training data set that has already been labeled with expected or correct
results.
Let’s look at the two of the most renowned supervised learning algorithms
used to develop machine learning models, in detail!