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Machine Learning Algorithms
Machine Learning Algorithms

Machine Learning Algorithms: A reference guide to popular algorithms for data science and machine learning

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Machine Learning Algorithms

Important Elements in Machine Learning

In this chapter, we're going to discuss some important elements and approaches which span through all machine learning topics and also create a philosophical foundation for many common techniques. First of all, it's useful to understand the mathematical foundation of data formats and prediction functions. In most algorithms, these concepts are treated in different ways, but the goal is always the same. More recent techniques, such as deep learning, extensively use energy/loss functions, just like the one described in this chapter, and even if there are slight differences, a good machine learning result is normally associated with the choice of the best loss function and the usage of the right algorithm to minimize it.

Data formats

In a supervised learning problem, there will always be a dataset, defined as a finite set of real vectors with m features each:

Considering that our approach is always probabilistic, we need to consider each X as drawn from a statistical multivariate distribution D. For our purposes, it's also useful to add a very important condition upon the whole dataset X: we expect all samples to be independent and identically distributed (i.i.d). This means all variables belong to the same distribution D, and considering an arbitrary subset of m values, it happens that:

The corresponding output values can be both numerical-continuous or categorical. In the first case, the process is called regression, while in the second, it is called classification. Examples of numerical outputs are:

Categorical examples are:

We define generic regressor, a vector...

Learnability

A parametric model can be split into two parts: a static structure and a dynamic set of parameters. The former is determined by choice of a specific algorithm and is normally immutable (except in the cases when the model provides some re-modeling functionalities), while the latter is the objective of our optimization. Considering n unbounded parameters, they generate an n-dimensional space (imposing bounds results in a sub-space without relevant changes in our discussion) where each point, together with the immutable part of the estimator function, represents a learning hypothesis H (associated with a specific set of parameters):

The goal of a parametric learning process is to find the best hypothesis whose corresponding prediction error is minimum and the residual generalization ability is enough to avoid overfitting. In the following figure, there's an...

Statistical learning approaches

Imagine that you need to design a spam-filtering algorithm starting from this initial (over-simplistic) classification based on two parameters:

Parameter Spam emails (X1) Regular emails (X2)
p1 - Contains > 5 blacklisted words 80 20
p2 Message length < 20 characters 75 25

 

We have collected 200 email messages (X) (for simplicity, we consider p1 and pmutually exclusive) and we need to find a couple of probabilistic hypotheses (expressed in terms of p1 and p2), to determine:

We also assume the conditional independence of both terms (it means that hp1 and hp2 contribute conjunctly to spam in the same way as they were alone).

For example, we could think about rules (hypotheses) like: "If there are more than five blacklisted words" or "If the message is less than 20 characters in...

Elements of information theory

A machine learning problem can also be analyzed in terms of information transfer or exchange. Our dataset is composed of n features, which are considered independent (for simplicity, even if it's often a realistic assumption) drawn from n different statistical distributions. Therefore, there are n probability density functions pi(x) which must be approximated through other n qi(x) functions. In any machine learning task, it's very important to understand how two corresponding distributions diverge and what is the amount of information we lose when approximating the original dataset.

The most useful measure is called entropy:

 

This value is proportional to the uncertainty of X and it's measured in bits (if the logarithm has another base, this unit can change too). For many purposes, a high entropy is preferable...

References

  • Russel S., Norvig P., Artificial Intelligence: A Modern Approach, Pearson
  • Valiant L., A Theory of the Learnable, Communications of the ACM, Vol. 27, No. 11 (Nov. 1984)
  • Hastie T., Tibshirani R., Friedman J., The Elements of Statistical Learning: Data Mining, Inference and, Prediction, Springer
  • Aleksandrov A.D., Kolmogorov A.N, Lavrent'ev M.A., Mathematics: Its contents, Methods, and Meaning, Courier Corporation

Data formats


In a supervised learning problem, there will always be a dataset, defined as a finite set of real vectors with m features each:

Considering that our approach is always probabilistic, we need to consider each X as drawn from a statistical multivariate distribution D. For our purposes, it's also useful to add a very important condition upon the whole dataset X: we expect all samples to be independent andidentically distributed (i.i.d). This means all variables belong to the same distribution D, and considering an arbitrary subset of m values, it happens that:

The corresponding output values can be both numerical-continuous or categorical. In the first case, the process is called regression, while in the second, it is called classification. Examples of numerical outputs are:

Categorical examples are:

We define generic regressor, a vector-valued function which associates an input value to a continuous output and generic classifier, a vector-values function whose predicted output is...

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Key benefits

  • Get started in the field of Machine Learning with the help of this solid, concept-rich, yet highly practical guide.
  • Your one-stop solution for everything that matters in mastering the whats and whys of Machine Learning algorithms and their implementation.
  • Get a solid foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide.

Description

In this book, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. The algorithms that are covered in this book are linear regression, logistic regression, SVM, naïve Bayes, k-means, random forest, TensorFlow and feature engineering. In this book, you will how to use these algorithms to resolve your problems, and how they work. This book will also introduce you to natural language processing and recommendation systems, which help you to run multiple algorithms simultaneously. On completion of the book, you will know how to pick the right machine learning algorithm for clustering, classification, or regression for your problem

Who is this book for?

This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.

What you will learn

  • Acquaint yourself with the important elements of machine learning
  • Understand the feature selection and feature engineering processes
  • Assess performance and error trade-offs for linear regression
  • Build a data model and understand how it
  • Learn to tune the parameters of SVMs
  • Implement clusters in a dataset
  • Explore the concept of Natural Processing Language and Recommendation Systems
  • Create a machine learning architecture from scratch

Product Details

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Publication date : Jul 24, 2017
Length: 360 pages
Edition : 1st
Language : English
ISBN-13 : 9781785889622
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Product Details

Publication date : Jul 24, 2017
Length: 360 pages
Edition : 1st
Language : English
ISBN-13 : 9781785889622
Category :
Languages :
Concepts :
Tools :

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Table of Contents

15 Chapters
A Gentle Introduction to Machine Learning Chevron down icon Chevron up icon
Important Elements in Machine Learning Chevron down icon Chevron up icon
Feature Selection and Feature Engineering Chevron down icon Chevron up icon
Linear Regression Chevron down icon Chevron up icon
Logistic Regression Chevron down icon Chevron up icon
Naive Bayes Chevron down icon Chevron up icon
Support Vector Machines Chevron down icon Chevron up icon
Decision Trees and Ensemble Learning Chevron down icon Chevron up icon
Clustering Fundamentals Chevron down icon Chevron up icon
Hierarchical Clustering Chevron down icon Chevron up icon
Introduction to Recommendation Systems Chevron down icon Chevron up icon
Introduction to Natural Language Processing Chevron down icon Chevron up icon
Topic Modeling and Sentiment Analysis in NLP Chevron down icon Chevron up icon
A Brief Introduction to Deep Learning and TensorFlow Chevron down icon Chevron up icon
Creating a Machine Learning Architecture Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(4 Ratings)
5 star 75%
4 star 0%
3 star 25%
2 star 0%
1 star 0%
Stefan Hildebrandt Oct 30, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I was searching for quite a while to find a math-based book. Something that requires algo knowledge and not simple copy/paste. I like the way he writes, as it is more a dictionary than a simple read. Something to hold next your desk, rather than a quick read.Definitely a recommendation by me!
Amazon Verified review Amazon
Antonio Gulli Sep 08, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is an extremely detailed book with both strong mathematical background and good python code. Very accurate and updated. From traditional machine learning to more advanced deep learning. Recommended
Amazon Verified review Amazon
Aniket Mar 08, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Book quality in terms of pages and binding is good.Pros:1. Book does justice to introduce you to the basics of Machine Learning algorithms.2. Mathematics is not kept at the center of the book, most of the concepts are explained into more of the theoretical sense than mathematically (This might be a disadvantage to the people looking at this book from a mathematical perspective).3. The good part of the book is, it explains the application of algorithms and techniques with python code examples.(sklearn is the library of choice mostly).Cons:1. Less focus on mathematical derivations of the algorithms.2. Less information about deep learning.But since this is just an introductory book, Cons are justifiable.
Amazon Verified review Amazon
Monica Nov 20, 2019
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I read in the reviews that this book gives strong mathematical background about machine learning which apparently it does not. The book throws some formulas without proper definition or explanation or background. In overall, it highlights and summarize scikit learn package of python .
Amazon Verified review Amazon
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