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Machine Learning Algorithms and Applications
Preface to Machine Learning PDF Guide
If you’re new to machine studying and you have no idea which ebook to begin from, then the reply is that this ebook.
If you already know among the theories in machine studying, however you have no idea learn how to write your personal algorithms, then once more you must begin from this ebook.
This ebook focuses on the supervised and unsupervised machine studying strategies. The principle goal of this ebook is to introduce these strategies in a easy and sensible means,
in order that they are often understood even by rookies to get profit from them.
In every chapter, we talk about the algorithms by which the chapter strategies work, and implement the algorithms in MATLAB®.
We selected MATLAB to be the principle programming language of the ebook as a result of it’s easy and extensively used amongst scientists; on the identical time, it helps the machine studying strategies by its statistics toolbox.
The ebook consists of 12 chapters, divided into two sections:
I: Supervised Learning Algorithms
II: Unsupervised Learning Algorithms
Within the first part, we talk about the choice bushes, rule-based classifiers, naïve Bayes classification, k-nearest neighbors, neural networks, linear discriminant evaluation, and assist vector machines.
Within the second part, we talk about the k-means, Gaussian combination mannequin, hidden Markov mannequin, and principal element evaluation within the context of dimensionality discount.
We now have written the chapters in such a means that each one are unbiased of each other. Which means the reader can begin from any chapter and perceive it simply.
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