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

Linear Discriminant is another method of dimensionality reduction where
we combine features or variables, rather than get rid of them altogether.
Kernel Principal Component is the third method for dimensionality
reduction. Here, variables are placed in a new set. This model will be non-
linear, and it will give us even better insight into the true parameters than
original data.

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Neural networks
Neural networks are a form of machine learning that is referred to as deep
learning. It’s probably the most advanced method of machine learning, and
truly understanding how it works might require a Ph.D. You could write an
entire book on machine learnings most technical type of model.
Neural networks are computer systems designed to mimic the path of
communication within the human brain. In your body, you have billions of
neurons that are all interconnected and travel up through your spine and
into your brain. They are attached by root-like nodes that pass messages
through each neuron one at a time all the way up the chain until it reaches
your brain.
While there is no way to replicate this with a computer yet, we take the
principle idea and apply it to computer neural networks to replicate the
ability to learn like a human brain learns; recognize patterns and inferring
information from the discovery of new information.

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In the case of the neural networks, as with all our machine learning models.
Information is processed through neural networks as numerical data. By
giving out numerical data values, we are giving it the power to use
algorithms to make predictions.
Just as with the neurons in the brain, data starts at the top and works its way
down, being first separated into nodes. The neural network uses nodes to
communicate through each layer. A neural network is comprised of three