Machine learning algorithms : popular algorithms for data science and machine learning / Giuseppe Bonaccorso.

Format:

Book

Author/Creator:

Bonaccorso, Giuseppe, author.

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

Physical Description:

1 online resource (514 pages)

Edition:

Second edition.

Place of Publication:

Birmingham ; Mumbai : Packt Publishing, 2018.

System Details:

text file

Summary:

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features Explore statistics and complex mathematics for data-intensive applications Discover new developments in EM algorithm, PCA, and bayesian regression Study patterns and make predictions across various datasets Book Description Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you'll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative. What you will learn Study feature selection and the feature engineering process Assess performance and error trade-offs for linear regression Build a data model and understand how it works by using different types of algorithm Learn to tune the parameters of Support Vector Machines (SVM) Explore the concept of natural language processing (NLP) and recommendation systems Create a machine learning architecture from scratch Who this book is for Machine Learning Algorithms is for you if you are a machine learning engineer, data engineer, or junior data scientist who wants to advance in the field of predictive analytics and machine learning. Familiarity with R and Python will be an added advantage for getting the best from this book. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. I...

Contents:

Cover

Title Page

Copyright and Credits

Dedication

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: A Gentle Introduction to Machine Learning

Introduction - classic and adaptive machines

Descriptive analysis

Predictive analysis

Only learning matters

Supervised learning

Unsupervised learning

Semi-supervised learning

Reinforcement learning

Computational neuroscience

Beyond machine learning - deep learning and bio-inspired adaptive systems

Machine learning and big data

Summary

Chapter 2: Important Elements in Machine Learning

Data formats

Multiclass strategies

One-vs-all

One-vs-one

Learnability

Underfitting and overfitting

Error measures and cost functions

PAC learning

Introduction to statistical learning concepts

MAP learning

Maximum likelihood learning

Class balancing

Resampling with replacement

SMOTE resampling

Elements of information theory

Entropy

Cross-entropy and mutual information

Divergence measures between two probability distributions

Chapter 3: Feature Selection and Feature Engineering

scikit-learn toy datasets

Creating training and test sets

Managing categorical data

Managing missing features

Data scaling and normalization

Whitening

Feature selection and filtering

Principal Component Analysis

Non-Negative Matrix Factorization

Sparse PCA

Kernel PCA

Independent Component Analysis

Atom extraction and dictionary learning

Visualizing high-dimensional datasets using t-SNE

Chapter 4: Regression Algorithms

Linear models for regression

A bidimensional example

Linear regression with scikit-learn and higher dimensionality

R2 score

Explained variance

Regressor analytic expression

Ridge, Lasso, and ElasticNet

Ridge

Lasso.

ElasticNet

Robust regression

RANSAC

Huber regression

Bayesian regression

Polynomial regression

Isotonic regression

Chapter 5: Linear Classification Algorithms

Linear classification

Logistic regression

Implementation and optimizations

Stochastic gradient descent algorithms

Passive-aggressive algorithms

Passive-aggressive regression

Finding the optimal hyperparameters through a grid search

Classification metrics

Confusion matrix

Precision

Recall

F-Beta

Cohen's Kappa

Global classification report

Learning curve

ROC curve

Chapter 6: Naive Bayes and Discriminant Analysis

Bayes' theorem

Naive Bayes classifiers

Naive Bayes in scikit-learn

Bernoulli Naive Bayes

Multinomial Naive Bayes

An example of Multinomial Naive Bayes for text classification

Gaussian Naive Bayes

Discriminant analysis

Chapter 7: Support Vector Machines

Linear SVM

SVMs with scikit-learn

Kernel-based classification

Radial Basis Function

Polynomial kernel

Sigmoid kernel

Custom kernels

Non-linear examples

ν-Support Vector Machines

Support Vector Regression

An example of SVR with the Airfoil Self-Noise dataset

Introducing semi-supervised Support Vector Machines (S3VM)

Chapter 8: Decision Trees and Ensemble Learning

Binary Decision Trees

Binary decisions

Impurity measures

Gini impurity index

Cross-entropy impurity index

Misclassification impurity index

Feature importance

Decision Tree classification with scikit-learn

Decision Tree regression

Example of Decision Tree regression with the Concrete Compressive Strength dataset

Introduction to Ensemble Learning

Random Forests

Feature importance in Random Forests

AdaBoost

Gradient Tree Boosting.

Voting classifier

Chapter 9: Clustering Fundamentals

Clustering basics

k-NN

Gaussian mixture

Finding the optimal number of components

K-means

Finding the optimal number of clusters

Optimizing the inertia

Silhouette score

Calinski-Harabasz index

Cluster instability

Evaluation methods based on the ground truth

Homogeneity

Completeness

Adjusted Rand Index

Chapter 10: Advanced Clustering

DBSCAN

Spectral Clustering

Online Clustering

Mini-batch K-means

BIRCH

Biclustering

Chapter 11: Hierarchical Clustering

Hierarchical strategies

Agglomerative Clustering

Dendrograms

Agglomerative Clustering in scikit-learn

Connectivity constraints

Chapter 12: Introducing Recommendation Systems

Naive user-based systems

Implementing a user-based system with scikit-learn

Content-based systems

Model-free (or memory-based) collaborative filtering

Model-based collaborative filtering

Singular value decomposition strategy

Alternating least squares strategy

ALS with Apache Spark MLlib

Chapter 13: Introducing Natural Language Processing

NLTK and built-in corpora

Corpora examples

The Bag-of-Words strategy

Tokenizing

Sentence tokenizing

Word tokenizing

Stopword removal

Language detection

Stemming

Vectorizing

Count vectorizing

N-grams

TF-IDF vectorizing

Part-of-Speech

Named Entity Recognition

A sample text classifier based on the Reuters corpus

Chapter 14: Topic Modeling and Sentiment Analysis in NLP

Topic modeling

Latent Semantic Analysis

Probabilistic Latent Semantic Analysis

Latent Dirichlet Allocation

Introducing Word2vec with Gensim

Sentiment analysis

VADER sentiment analysis with NLTK

Summary.

Chapter 15: Introducing Neural Networks

Deep learning at a glance

Artificial neural networks

MLPs with Keras

Interfacing Keras to scikit-learn

Chapter 16: Advanced Deep Learning Models

Deep model layers

Fully connected layers

Convolutional layers

Dropout layers

Batch normalization layers

Recurrent Neural Networks

An example of a deep convolutional network with Keras

An example of an LSTM network with Keras

A brief introduction to TensorFlow

Computing gradients

Classification with a multilayer perceptron

Image convolution

Chapter 17: Creating a Machine Learning Architecture

Machine learning architectures

Data collection

Normalization and regularization

Dimensionality reduction

Data augmentation

Data conversion

Modeling/grid search/cross-validation

Visualization

GPU support

A brief introduction to distributed architectures

Scikit-learn tools for machine learning architectures

Pipelines

Feature unions

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Index.

Notes:

Previous edition published: 2017.

Description based on print version record.

ISBN:

9781789345483

1789345480

OCLC:

1055555812