In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge.
This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general.
This book is meant to be an introduction into machine learning. It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book.
Key Features:
- Describes real world problems that can be solved using Machine Learning
- Provides methods for directly applying Machine Learning techniques to concrete real world problems
- Demonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R

lgrsnf/R:\062020\TF\Machine learning and its applications - 9780429828744.pdf

nexusstc/Machine Learning and its Applications/2b3d937d9adebe154a286d9ea965d535.pdf

zlib/Computers/Computer Science/Peter Wlodarczak (Author)/Machine Learning and its Applications_5954427.pdf

Peter Wlodarczak (Author)

Ashgate Publishing Limited

Taylor & Francis Ltd

Gower Publishing Ltd

United Kingdom and Ireland, United Kingdom

CRC Press (Unlimited), Boca Raton, 2019

First edition, Boca Raton, FL, 2020

First edition, Boca Raton, 2019

1, 2019-11-04

1, US, 2019

lg2785030

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Contents
Preface
SECTION I: INTRODUCTION
Introduction
Data mining
Data mining steps
Data collection
Data pre-processing
Data analysis
Data post-processing
Machine learning basics
Supervised learning
Unsupervised learning
Semi-supervised learning
Function approximation
Generative and discriminative models
Evaluation of learner
SECTION II: MACHINE LEARNING
Data pre-processing
Feature extraction
Sampling
Data transformation
Outlier removal
Data deduplication
Relevance filtering
Normalization, discretization and aggregation
Entity resolution
Supervised learning
Classification
Regression analysis
Logistic regression
Evaluation of learner
Evaluating a learner
Unsupervised learning
Types of clustering
k-means clustering
Hierarchical clustering
Visualizing clusters
Evaluation of clusters
Semi-supervised learning
7.1 Expectation maximization
7.2 Pseudo labeling
SECTION III: DEEP LEARNING
Deep Learning
8.1 Deep Learning Basics
8.2 Convolutional neural networks
8.3 Recurrent neural networks
8.4 Restricted Boltzmann machines
8.5 Deep belief networks
8.6 Deep autoencoders
SECTION IV: LEARNING TECHNIQUES
Learning techniques
Learning issues
Cross-validation
Ensemble learning
Reinforcement learning
Active learning
Machine teaching
Automated machine learning
SECTION V: MACHINE LEARNING APPLICATIONS
Machine Learning Applications
Anomaly detection
Biomedicale applications
Natural language processing
Other applications
Future development
Research directions
References
Index

"This book describes Machine Learning techniques and algorithms that have been used in recent real-world application. It provides an introduction to Machine Learning, describes the most widely used techniques and methods. It also covers Deep Learning and related areas such as function approximation or. The book gives real world examples where Machine Learning techniques are applied and describes the basic math and the commonly used learning techniques"-- Provided by publisher

2020-09-27