Machine Learning Books

Introduction to machine learning

Nils J. Nilsson

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Machine Learning

Jaydip Sen

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Undergraduate Fundamentals of Machine Learning

William J. Deuschle

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Machine Learning - Supervised Techniques

Sepp Hochreiter

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Machine learning - The power and promise of computers that learn by example

Royal Society

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Machine Learning

MRCET

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The Foundation for Best Practices in Machine Learning

FBPML

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Machine Learning Tutorial

Wei-Lun Chao

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A non-technical introduction to machine learning

Olivier Colliot

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Artificial Intelligence and Machine Learning Approaches in Digital Education - A Systematic Revision

Hussan Munir, Bahtijar Vogel and Andreas Jacobsson

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Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions

Raffaele Cioffi, Marta Travaglioni, Giuseppina Piscitelli, Antonella Petrillo and Fabio De Felice

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Rules of Machine Learning - Best Practices for ML Engineering

Martin Zinkevich

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Best Practices for Machine Learning Applications

Brett Wujek, Patrick Hall, and Funda Gunes

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Supervised Machine Learning: A Review of Classification Techniques

S. B. Kotsiantis

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The fundamentals of machine learning

Jay Wilpon, David Thomson, Srinivas Bangalore, Patrick Haffner and Michael Johnston

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An Introduction to Machine Learning

Solveig Badillo, Balazs Banfai, Fabian Birzele and others

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How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different? (Article)

Tanya Tiwari, Tanuj Tiwari and Sanjay Tiwari

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Machine Learning in Artificial Intelligence - Towards a Common Understanding (Article)

Niklas Kühl, Marc Goutier, Robin Hirt, Gerhard Satzger

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Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature (Article)

Andreas K Triantafyllidis and Athanasios Tsanas

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Overview of Machine Learning Tools and Libraries

Daniel Pop and Gabriel Iuhasz

ReadDownloadAlgorithms in Machine Learning Books

In the world of machine learning, algorithms are an essential part of the machine learning process, and understanding them can be critical to developing innovative solutions in different areas.

Algorithms in machine learning are a series of defined steps that allow machines to learn from data and improve their performance over time.

If you are interested in learning more about this topic, we invite you to explore our selection of free books and articles on machine learning algorithms.

Online gradient descent learning algorithm

Yiming Ying and Massimiliano Pontil

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Types of Machine Learning Algorithms

Taiwo Oladipupo Ayodele

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Clustering Algorithms: A Comparative Approach

Mayra Z. Rodriguez, Cesar H. Comin, Dalcimar Casanova and others

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Dbscan - Fast Density-based Clustering with R

Michael Hahsler, Matthew Piekenbrock and Derek Doran

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Methods of Hierarchical Clustering

Fionn Murtagh and Pedro Contreras

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Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models

Xinhui Zhang, Xun Shen and Tinghui Ouyang

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A review of Machine Learning (ML) algorithms used for modeling travel mode choice

Pineda-Jaramillo and Juan D

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A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms

Shahneela Pitafi, Toni Anwar and Zubair Sharif

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K-Means Clustering and Related Algorithms

Ryan P. Adams

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Selection of K in K-means clustering

D T Pham, S. S. Dimov, and C D Nguyen

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k-Nearest Neighbour Classifiers

Pádraig Cunningham and Sarah Jane Delany

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DBSCAN: A simple fast DBSCAN algorithm for big data

Shaoyuan Weng, Jin Gou and Zongwen Fan

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KNN Classification With One-Step Computation

Shichao Zhang and Jiaye Li

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Hierarchical Clustering (Article)

Ryan P. Adams

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Implementation of Decision Tree Algorithm to Classify Knowledge Quality in a Knowledge Intensive System

Casper Kaun, N.Z Jhanjhi, Wei Wei Goh and Sanath Sukumaran

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Supervised Machine Learning Algorithms - Classification and Comparison (Article)

Osisanwo F.Y, Akinsola J.E.T, Awodele O and others

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Random Forest Classifiers :A Survey and Future Research Directions (Article)

Vrushali Y Kulkarni and Pradeep K Sinha

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Combining Hierarchical Clustering and Machine Learning to Predict High-Level Discourse Structure (Article)

Caroline Sporleder and Alex Lascarides

ReadDownloadSupervised Learning Books

Supervised learning is one of the most popular and widely used techniques in the field of Machine Learning. It is a type of learning in which an algorithm is trained using a labeled data set to learn to make accurate predictions or classifications.

Supervised learning is used in a wide variety of Machine Learning applications, such as image classification, email spam detection, fraud detection in financial transactions, and many others.

Learn more about this powerful and versatile technique with the following free supervised learning books and articles in PDF format.

Supervised Learning - An Introduction

Michael Biehl

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Supervised Learning Techniques - A comparison of the Random Forest and the Support Vector Machine

Jonni Fidler Dennis and Lukas Arnroth

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Supervised Machine Learning Techniques: An Overview with Applications to Banking

Linwei Hu, Jie Chen, Joel Vaughan, Hanyu Yang and others

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Supervised Machine Learning: A Brief Introduction

Seemant TIWARI

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Supervised Machine Learning

Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten and Thomas B. Schön

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Unsupervised learning is another essential technique used in Machine Learning. Unlike supervised learning, where the algorithm is trained using a labeled data set, in unsupervised learning the algorithm is prepared using an unlabeled data set.

In unsupervised learning, the algorithm is responsible for finding patterns in the input data on its own, without being told what to look for. This technique is especially useful in situations where there is no labeled training data set available.

This technique is used in a wide variety of Machine Learning applications, such as customer segmentation, data clustering, anomaly detection, and many others. You can learn a little more with the following unsupervised learning books and articles in PDF format.

Unsupervised learning - a systematic literature review

Salim Dridi

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Unsupervised Learning

Wei Wu

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Discovery of Course Success Using Unsupervised Machine Learning Algorithms

Emre CAM and Muhammet Esat OZDAG

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Unsupervised learning (Article)

Hannah Van Santvliet

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Deep Learning of Representations for Unsupervised and Transfer Learning

Yoshua Bengio

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Unsupervised Feature Learning and Deep Learning - A Review and New Perspectives

Yoshua Bengio, Aaron Courville, and Pascal Vincent

ReadDownloadDeep Learning Books

Deep learning is a machine learning technique that uses artificial neural networks to learn from large data sets and improve their ability to perform complex tasks.

It has become increasingly popular in recent years due to its ability to tackle complex problems in different areas, from medicine to robotics. 

It has also enabled significant advances in areas such as speech recognition and computer vision. You can learn more about this topic with the following books and articles on deep learning.

The Little Book of Deep Learning

François Fleuret

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Neural Networks and Deep Learning

Michael Nielsen

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Deep learning in neural networks: An overview

Jürgen Schmidhuber

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List of Deep Learning Models

Amir Mosavi, Sina Ardabili, and Annamária R. Várkonyi-Kóczy

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Machine learning and deep learning (Article)

Christian Janiesch, Patrick Zschech and Kai Heinrich

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Deep Learning Limitations and Flaws (Article)

Bahman Zohuri and Masoud Moghaddam

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Deep Learning Techniques: An Overview (Article)

Amitha Mathew, P.Amudha and S.Sivakumari

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The Limitations of Deep Learning in Adversarial Settings

Nicolas Papernot, Patrick McDaniel, Somesh Jha and others

ReadDownloadMachine Learning and Database Books

Machine learning and databases are two closely related topics. In simple terms, databases are an essential tool for storing and organizing large data sets, while Machine Learning is a technique for analyzing and extracting useful information from that data.

Together, machine learning and databases are essential for processing and analyzing large data sets. For example, machine learning algorithms can be used to analyze data stored in a database and provide useful information to users.

In addition, databases can be used to store and organize the data needed to train machine learning algorithms. Learn more about this interesting relationship with the following books and articles on machine learning and databases.

Data Science and Machine Learning

Dirk P. Kroese, Zdravko I. Botev, Thomas Taimre and Radislav Vaisman

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Handbook Of Artificial Intelligence And Big Data Applications In Investments

Larry Cao

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On practical machine learning and data analysis

Daniel Gillblad

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Machine Learning with Big Data - Challenges and Approaches

Alexandra L’Heureux, Katarina Grolinger, Hany F. ElYamany, Miriam A. M. Capretz

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Machine Learning for Database Management Systems

Sai Tanishq N.

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A review on the significance of machine learning for data analysis in big data

Vishnu Vandana Kolisetty and Dharmendra Singh Rajput

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UDO: Universal Database Optimization using Reinforcement Learning

Junxiong Wang, Immanuel Trummer and Debabrota Basu

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Exploration of Approaches for In-Database ML

Steffen Kläbe, Stefan Hagedorn and Kai-Uwe Sattler

ReadDownloadNeural Networks Books

Neural networks are computational systems that are inspired by the workings of the human brain and are used to learn from large data sets and perform complex tasks in an automated manner.

They are commonly used in computer vision, natural language processing, and robotics. In addition, deep neural networks have enabled significant advances in the field of deep learning.

If you would like to learn more, we invite you to take a look at the following books and articles on neural networks that we have located for you in PDF format.

Natural Language Processing

Jacob Eisenstein

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Natural Language Processing

Ann Copestake

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Introduction to natural language processing

R. Kibble

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Natural Language Processing

SSCASC

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Natural Language Processing Advancements By Deep Learning - A Survey

Amirsina Torf, Rouzbeh A. Shirvani, Yaser Keneshloo, Nader Tavaf, and Edward A

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