Classical machine learning algorithms

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Classical machine learning algorithms

Introduction:= Content'What this Book CoversThis book covers the building blocks of the most common methods in machine learning. This set of methods i

Classical machine learning algorithms is like a toolbox for machine learning engineers. Those entering the held of machine teaming should feel comfortable with this toolbox so they have th

e right tool for a variety of tasks. Each chapter in this book corresponds to a stegte machine learning method or group of methods In other words, eac Classical machine learning algorithms

h chapter focuses on a single tool within the ML toolboxIn my experience, the best way to become comfortable with these methods is tosee them derived

Classical machine learning algorithms

from scratch, both in theory and in code. The purpose of this book is to provide those derivations. Each chapter is broken into three sections. The co

Introduction:= Content'What this Book CoversThis book covers the building blocks of the most common methods in machine learning. This set of methods i

Classical machine learning algorithms from scratch using Python The imptemeoraticw sections demonstrate how to appty the methods usmg packages in python like sc ikit-learn. st atsrode Is.

and tensor flow.Why this BookThere are many great books on machine learning written by more knowledgeable authors and covering a broader range of topi Classical machine learning algorithms

cs. In particular. I would suggest An Intr oduction to Statistical Learning. Elements of Statistical Leading and Pattern Recognition ?r»,i Machine lea

Classical machine learning algorithms

rr ng. all of which arc available cnline for free.While those books provide a conceptual overview of machine learnmg and the theory behind Its methods

Introduction:= Content'What this Book CoversThis book covers the building blocks of the most common methods in machine learning. This set of methods i

Classical machine learning algorithms thms independently. Continuing tb< toolbox analogy. this book is intended as a user guide: it is not designed to teach users broad practices of the he

ld but rather how each tool works at a micro level.Who this Book is forThis book is for readers looking to learn new machine learning algorithms or un Classical machine learning algorithms

derstand algorithms at a deeper level. Specifically. It Is intended for readers interested in seeing machine learning algorithms derived from start to

Classical machine learning algorithms

hn«sh. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or. seeing the

Introduction:= Content'What this Book CoversThis book covers the building blocks of the most common methods in machine learning. This set of methods i

Classical machine learning algorithms antages of each one.This book will be most helpful for those with practice in basic modeling. It does not review best practices-such as feature engine

ering or balancing response variables-or discuss in depth when certain models arc more appropriate than others. Instead, it focuses on the elements of Classical machine learning algorithms

those models.What Readers Should KnowThe concept sections of this book primarily require knowledge of calcukis. though some require an understanding

Classical machine learning algorithms

of probability (think maximum likelihood and Bayes Rule} and basic linear algebra (think matrix operations and dot products}. The appendix reviews the

Introduction:= Content'What this Book CoversThis book covers the building blocks of the most common methods in machine learning. This set of methods i

Classical machine learning algorithms n the appendix as well The concept sections do not require any knowledge of programming.The construction and code sections of this book use some basic

python. The construction sections require understanding of the corresponding content sections and familiarity creating functions and classes in Pytho Classical machine learning algorithms

n The code sections require neither.Where to Ask Questions or Give FeedbackYou can raise an issue here or email me at dafrdmaixggmadxom.Table of Conte

Classical machine learning algorithms

nts1Ordinary Linear Regression1The Loss-Mimmization Perspective2The Likelihood-Maximization Perspective2Linear Regression Extensions1Regularized Regre

Introduction:= Content'What this Book CoversThis book covers the building blocks of the most common methods in machine learning. This set of methods i

Classical machine learning algorithms ithm2 Fisher s Linear Discriminant4Generative Classification(Linear and Quadratic Discriminant Analysis. Naive 8ayesl5Decision Trees1Regression Trees2

Classification Treesá Tree Ensemble Methods1Gassing2Random Forestsa Boosting Classical machine learning algorithms

Introduction:= Content'What this Book CoversThis book covers the building blocks of the most common methods in machine learning. This set of methods i