A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | R | S | T | U | V | W | Z |
|eBooks| = 490 |
 |
Please check licensing before downloading documents using the below listed links and note, that the validity of links might change quickly. |
A
|
 |
A Beginner’s Guide to R |
228 Pages |
2009 |
|
A Brief Introduction to Neural Networks |
286 Pages |
2005 |
|
A Computational Approach to Statistics |
492 Pages |
2006 |
|
A Course in Machine Learning |
189 Pages |
2013 |
|
A Course in Machine Learning |
229 Pages |
2012 |
|
A Course in Machine Learning |
191 Pages |
2014 |
|
A Course in Time Series Analysis |
325 Pages |
2014 |
|
A Field Guide to Genetic Programming |
250 Pages |
2008 |
|
A First Course in Design and Analysis of Experiments |
679 Pages |
2010 |
|
A First Course on Time Series Analysis |
364 Pages |
2012 |
|
A First Encounter With Machine Learning 2010 |
93 Pages |
2010 |
|
A First Encounter With Machine Learning |
93 Pages |
2011 |
|
A Gentle Introduction to Apache Spark |
50 Pages |
2017 |
 |
A Handbook of Statistical Analyses Using R |
207 Pages |
2005 |
|
A History of Mathematical Notations |
870 Pages |
1993 |
 |
A Little Book of R For Bayesian Statistics |
27 Pages |
2015 |
 |
A Little Book of R For Bioinformatics |
77 Pages |
2011 |
 |
A Little Book of R For Multivariate Analysis |
51 Pages |
2013 |
 |
A Little Book of R For Time Series |
75 Pages |
2014 |
|
A Modern Introduction to Probability and Statistics |
483 Pages |
2005 |
|
A Nonparametric Statistical Approach to Clustering via Mode Identification |
37 Pages |
2007 |
|
A Probabilistic Theory of Pattern Recognition |
661 Pages |
1995 |
|
A Probability Course for the Actuaries |
599 Pages |
2014 |
 |
A Programmer’s Guide to Data Mining |
305 Pages |
2013 |
 |
A Tiny Handbook of R |
94 Pages |
2011 |
 |
A Trio of Texts |
|
|
 |
Advanced Data Analysis from an Elementary Point of View |
697 Pages |
2014 |
 |
Advanced Linear Models for Data Science |
70 Pages |
2015 |
 |
Advanced Machine Learning With Python |
278 Pages |
2018 |
 |
Advanced R |
|
2014 |
|
Agile Data Science |
177 Pages |
2014 |
 |
AI Algorithms Data Structures and Idioms |
463 Pages |
2009 |
|
Air University Sampling and Surveying Handbook |
102 Pages |
2002 |
|
Algorithm Design |
432 Pages |
2005 |
|
Algorithms and Data Structures |
295 Pages |
2007 |
|
Algorithms for Clustering Data |
334 Pages |
1988 |
|
Algorithms for Reinforcement Learning 2013 |
98 Pages |
2013 |
|
Algorithms for Reinforcement Learning |
98 Pages |
2010 |
|
Algorithms |
318 Pages |
2006 |
|
All of Nonparametric Statistics |
271 Pages |
2006 |
|
An Example of Statistical Data Analysis |
147 Pages |
2014 |
|
An Introduction to Copulas |
276 Pages |
2006 |
|
An Introduction to Genetic Algorithms |
162 Pages |
1999 |
|
An Introduction to Graphical Models |
102 Pages |
1997 |
|
An Introduction to Information Retrieval |
569 Pages |
2009 |
|
An Introduction to Mathematical Optimal Control Theory |
126 Pages |
2014 |
|
An Introduction to Probability Theory and ist Applications |
525 Pages |
1968 |
 |
An Introduction to R |
106 Pages |
2013 |
|
An Introduction to Sparse Stochastic Processes |
379 Pages |
2014 |
|
An Introduction to Statistical Inference and Its Applications with R |
459 Pages |
2008 |
 |
An Introduction to Statistical Learning 4th |
440 Pages |
2014 |
 |
An Introduction to Statistical Learning |
441 Pages |
2013 |
 |
An Introduction to Statistics |
192 Pages |
2015 |
|
An Introduction to Stochastic Modeling |
646 Pages |
1998 |
|
An Introduction to Stochastic Processes in Continuous Time |
143 Pages |
2014 |
 |
Analysing spatial point patterns in R |
232 Pages |
2010 |
|
AnalyticBridge Data Science eBook |
123 Pages |
2013 |
|
Apache Hadoop YARN |
337 Pages |
2014 |
|
Applied Bayesian Econometrics for Central Bankers |
145 Pages |
2012 |
 |
Applied Data Science |
141 Pages |
2014 |
|
Applied Functional Data Analysis – Methods and Case Studies |
201 Pages |
2002 |
|
Applied Multivariate Statistical Analysis |
393 Pages |
2007 |
|
Applied Numerical Computing |
274 Pages |
2011 |
|
Applied Numerical Linear Algebra |
421 Pages |
1996 |
|
Art and Visual Perception |
263 Pages |
1974 |
|
Artificial Intelligence – A Modern Approach – 2 |
1112 Pages |
2003 |
|
Artificial Intelligence – A Modern Approach |
946 Pages |
1995 |
|
Artificial Intelligence A Guide to Intelligent Systems |
435 Pages |
2005 |
|
Artificial Intelligence A Modern Approach – 1995 |
946 Pages |
1995 |
|
Artificial Intelligence A Modern Approach |
1152 Pages |
2010 |
|
Artificial Intelligence: A Modern Approach |
|
1995 |
|
Artificial Intelligence: Foundations of Computational Agents |
|
2010 |
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Automate the Boring Stuff with Python |
|
2015 |
B
|
|
Basic Econometrics |
1003 Pages |
2004 |
|
Basic Probability Theory |
350 Pages |
2008 |
|
Basics of Statistics |
83 Pages |
2002 |
 |
Bayesian Computation with R |
304 Pages |
2009 |
|
Bayesian Programming |
208 Pages |
2003 |
|
Bayesian Reasoning and Machine Learning 2014 |
672 Pages |
2014 |
|
Bayesian Reasoning and Machine Learning 2015 |
680 Pages |
2014 |
|
Bayesian Reasoning and Machine Learning |
672 Pages |
2013 |
 |
Bayesian Statistical Modelling |
598 Pages |
2006 |
|
Beautiful Data – The Stories Behind Elegant Data Solutions |
384 Pages |
2009 |
|
Big Data Now |
131 Pages |
2012 |
|
Big Data, Data Mining, and Machine Learning – Dean |
289 Pages |
2014 |
|
Big Data, Data Mining, and Machine Learning |
289 Pages |
2014 |
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Biplots in Practice |
|
2010 |
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BitBootcamp – Mathematics & Statistics |
|
|
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Building Machine Learning Systems with Python |
290 Pages |
2018 |
|
Business Cases mit SAP HANA |
595 Pages |
2013 |
C
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Categorical Data Analysis |
721 Pages |
2002 |
|
Causality – Models, Reasoning and Inference |
386 Pages |
2000 |
|
Causality – Objectives and Assessment |
393 Pages |
2011 |
|
Causality in Time Series |
152 Pages |
2013 |
|
Causation and Prediction Challenge |
292 Pages |
2010 |
|
Causation Prediction and Search |
546 Pages |
1993 |
|
Clever Algorithms – Nature-Inspired Programming Recipes |
454 Pages |
2012 |
|
Clever Algorithms – Statistical Machine Learning Recipes |
|
2013 |
|
Collaborative Statistics |
728 Pages |
2013 |
|
Combinatorial Stochastic Processes |
251 Pages |
2006 |
|
Combining Classification Algorithms |
195 Pages |
1999 |
|
Community Detection in Graphs |
103 Pages |
2010 |
|
Computational Complexity – A Modern Approach |
489 Pages |
2007 |
|
Computational Methods in Statistics and Economics |
512 Pages |
2004 |
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Computational statistics – an introduction to R |
232 Pages |
2008 |
|
Computer Age Statistical Inference – Algorithms Evidence and Data Science |
493 Pages |
2017 |
|
Computer Vision – Algorithms and Applications |
979 Pages |
2010 |
|
Concise Computer Vision |
441 Pages |
2010 |
|
Constraint-Based Scheduling – Modeling and Filtering |
163 Pages |
2014 |
|
Convergence of Stochastic Processes |
223 Pages |
1984 |
|
Convex Optimization |
730 Pages |
2009 |
|
Counterexamples in Analysis |
220 Pages |
1965 |
|
Customer Analytics for Dummies |
|
2013 |
D
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D3 Tips and Tricks |
598 Pages |
2015 |
 |
Data Analysis for the Life Sciences |
466 Pages |
2015 |
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Data Analysis Using Regression and Multilevel Hierarchical Models |
651 Pages |
2007 |
|
Data Classification |
64 Pages |
2015 |
|
Data Driven |
28 Pages |
2015 |
|
Data Journalism Handbook |
|
2012 |
|
Data Jujitsu – The Art of Turning Data into Product |
|
2012 |
|
Data Mining – Concepts and Techniques |
772 Pages |
2006 |
 |
Data Mining – Practical Machine Learning Tools and Techniques – 2 |
558 Pages |
2005 |
 |
Data Mining – Practical Machine Learning Tools and Techniques |
665 Pages |
2011 |
|
Data Mining Algorithms In R |
|
2015 |
|
Data Mining and Analysis – Fundamental Concepts and Algorithms |
607 Pages |
2014 |
|
Data Mining and Analysis in Internet Advertising |
44 Pages |
2012 |
|
Data Mining and Analysis |
659 Pages |
2013 |
 |
Data Mining and Business Analytics with R |
361 Pages |
2013 |
|
Data Mining and Knowledge Discovery Handbook |
1306 Pages |
2010 |
|
Data Mining Concepts and Techniques |
772 Pages |
2006 |
|
Data Mining For The Masses |
264 Pages |
2012 |
|
Data Mining Multimedia Soft Computing and Bioinformatics |
420 Pages |
2003 |
|
Data mining techniques – for marketing, sales, and customer |
672 Pages |
2004 |
|
Data Mining Techniques |
672 Pages |
2004 |
|
Data Mining with Decision Trees |
263 Pages |
2008 |
 |
Data Mining with R |
289 Pages |
2011 |
 |
Data Mining With Rattle and R |
395 Pages |
2011 |
 |
Data Mining with Rattle |
161 Pages |
2007 |
|
Data Mining |
|
2005 |
 |
Data Science Book V2 |
183 Pages |
2013 |
 |
Data Science Book V3 |
196 Pages |
2013 |
 |
Data Science Central – Comprehensive List of Data Science Resources |
|
|
|
Data Science for Business |
409 Pages |
2013 |
|
Data Science Live Book |
|
2016 |
|
Data Science: An Introduction |
|
2015 |
|
Data Structures and Algorithms |
112 Pages |
2008 |
 |
Data Visualization for Social Science |
|
2017 |
|
Data Visualization with JavaScript – jsDataV.is |
|
2014 |
|
Datablending for Dummies |
|
2015 |
|
Data-Intensive Text Processing with MapReduce |
175 Pages |
2010 |
|
Deep Learning |
|
2015 |
|
Deep Learning |
|
2014 |
|
Deep Learning – Methods and Applications |
134 Pages |
2014 |
 |
Deep Learning Tutorial |
153 Pages |
2014 |
|
Deep Learning |
134 Pages |
2014 |
|
Design and Analysis of Algorithms |
95 Pages |
2008 |
 |
Die Sprache R |
40 Pages |
2010 |
 |
Digital History Methods in R |
|
2014 |
|
Discrete Stochastic Processes |
333 Pages |
2009 |
|
Disruptive Possibilities – How Big Data Changes Everything |
77 Pages |
2013 |
 |
Dive Into Python 3 |
495 Pages |
2009 |
 |
Dynamic Linear Models with R |
186 Pages |
2007 |
E
|
|
Econometric Analysis |
827 Pages |
2002 |
|
Econometrics – Hansen |
387 Pages |
2015 |
 |
Econometrics In R |
50 Pages |
2008 |
|
Econometrics |
379 Pages |
2014 |
 |
Einfuehrung in die Statistik mit R |
558 Pages |
2006 |
 |
Elementary Statistics with R |
|
2014 |
|
Elements of Forecasting |
748 Pages |
2006 |
 |
Empirical Software Engineering using R |
265 Pages |
2016 |
|
Encyclopedia of Machine Learning |
1059 Pages |
2011 |
|
Ensemble Methods in Data Mining |
126 Pages |
2010 |
|
Essentials of Metaheuristics |
253 Pages |
2013 |
|
Essentials of Stochastic Processes |
226 Pages |
2011 |
|
Evolved to Win |
193 Pages |
2011 |
|
Exploratory Data Analysis – Past Present and Future |
102 Pages |
1993 |
|
Exploratory Factor Analysis |
459 Pages |
1997 |
F
|
|
Forecasting in Economics, Business, Finance and Beyond |
607 Pages |
2015 |
|
Forecasting: Principles and Practice |
|
2012 |
|
Foundations of Data Science 2016 |
439 Pages |
2016 |
|
Foundations of Data Science |
419 Pages |
2014 |
|
Foundations of Neural Networks Fuzzy Systems and Knowledge Engineering |
581 Pages |
1998 |
|
Foundations of Soft Case-Based Reasoning |
299 Pages |
2004 |
|
Foundations of Statistical Natural Language Processing |
704 Pages |
1999 |
|
Foundations of the Theory of Probability |
47 Pages |
1956 |
 |
Free Books for Learning Data Mining & Data Analysis |
|
|
 |
Free Data Mining Books |
|
|
|
Frequent Pattern Mining |
85 Pages |
2014 |
 |
From Linear Models to Machine Learning |
247 Pages |
2016 |
|
From Natural Language to Soft Computing |
226 Pages |
2008 |
|
Frontiers in Massive Data Analysis |
190 Pages |
2013 |
 |
Functional programming and unit testing for data munging with R |
63 Pages |
2016 |
|
Fundamental Methods of Mathematical Economics |
679 Pages |
1984 |
|
Fundamental Numerical Methods and Data Analysis |
|
2003 |
|
Fundamentals of Predictive Text Mining |
232 Pages |
2010 |
G
|
|
Gaussian Process Models |
205 Pages |
2006 |
|
Gaussian Processes for Machine Learning |
266 Pages |
2006 |
|
Generalised Interaction Mining |
349 Pages |
2010 |
 |
Generalized Additive Models – an introduction with R |
397 Pages |
2006 |
|
Generative Algorithms Using Grasshopper |
179 Pages |
2010 |
 |
Grafiken und Statistik in R |
240 Pages |
2010 |
|
Graph Algorithms |
453 Pages |
2014 |
 |
Graph Databases 2E |
238 Pages |
2015 |
 |
Graph Databases |
223 Pages |
2013 |
|
Graph Theory – Diestel |
322 Pages |
2000 |
|
Graph Theory – Harary |
281 Pages |
1969 |
|
Graph Theory with Applications |
270 Pages |
1082 |
|
Graph Theory |
114 Pages |
2013 |
|
Graphical Models Exponential Families and Variational Inference |
305 Pages |
2008 |
|
Graphs and Matrices |
175 Pages |
2010 |
|
Grinstead and Snell’s Introduction to Probability |
518 Pages |
2006 |
 |
Grundlagen der Datenanalyse mit R – 2011 |
325 Pages |
2011 |
H
|
|
Hadoop – The Definitive Guide |
526 Pages |
2ßß9 |
|
Hadoop for Dummies |
67 Pages |
2012 |
|
Hadoop Illuminated |
70 Pages |
2013 |
 |
Hadoop V3 |
643 Pages |
2012 |
|
Handbook of Biological Statistics |
291 Pages |
2008 |
|
Handbook of Computational Econometrics |
516 Pages |
2009 |
|
Handbook of Graph Drawing and Visualization |
|
2013 |
|
Handbook of Survival Analysis |
635 Pages |
2013 |
|
Handbook on Statistical Disclosure Control |
216 Pages |
2010 |
|
Handbook Statistical Foundations of Machine Learning |
267 Pages |
|
 |
Handling and Processing Strings in R |
112 Pages |
2013 |
 |
Handling Strings with R |
|
2018 |
I
|
|
Inductive Logic Programming |
311 Pages |
1994 |
|
Information Theory, Inference, and Learning Algorithms |
640 Pages |
2005 |
|
Information Visualization – Perception for Design – 2 |
513 Pages |
2004 |
|
Interactive Data Visualization for the Web |
268 Pages |
2013 |
|
International Handbook of Survey Methodology |
558 Pages |
2008 |
|
Interpretable Machine Learning |
|
2018 |
|
Introduction to Algorithms 3rd |
1313 Pages |
2009 |
|
Introduction to Algorithms |
984 Pages |
2001 |
|
Introduction to Data Mining |
792 Pages |
2006 |
|
Introduction to Empirical Bayes |
142 Pages |
2017 |
|
Introduction to GPUs for Data Analytics |
39 Pages |
2017 |
|
Introduction to Information Retrieval |
569 Pages |
2009 |
|
Introduction to Linear Algebra |
586 Pages |
2009 |
|
Introduction to Machine Learning – Cambridge |
234 Pages |
2008 |
|
Introduction to Machine Learning – Shashua |
109 Pages |
2008 |
|
Introduction to Machine Learning – Smola |
234 Pages |
2008 |
|
Introduction to Machine Learning – Standford |
188 Pages |
2005 |
|
Introduction to Machine Learning for Fraud Prevention |
17 Pages |
2018 |
|
Introduction to Machine Learning |
579 Pages |
2010 |
|
Introduction to Modern Time Series Analysis |
276 Pages |
2007 |
|
Introduction to Online Convex Optimization |
178 Pages |
2015 |
 |
Introduction to Probability and Statistics Using R |
412 Pages |
2011 |
|
Introduction to Probability Models – Students Manual |
59 Pages |
2010 |
|
Introduction to Probability Models |
801 Pages |
2010 |
 |
Introduction to Programming Econometrics with R |
50 Pages |
2014 |
 |
Introduction to Python for Econometrics Statistics and Data Analysis |
405 Pages |
2014 |
|
Introduction to State Space Time Series Analysis |
189 Pages |
2007 |
|
Introduction to Statistical Learning Theory |
39 Pages |
2004 |
 |
Introduction to Statistical Modelling in R |
115 Pages |
2012 |
 |
Introduction to Statistical Thinking |
324 Pages |
2011 |
 |
Introduction to Statistical Thought |
475 Pages |
2013 |
|
Introduction to Statistics and Data Analysis for Physicists |
412 Pages |
2010 |
|
Introduction to Statistics |
695 Pages |
2014 |
|
Introduction to Stochastic Processes – Lecture Notes |
107 Pages |
2010 |
|
Introduction to Stochastic Processes |
252 Pages |
2006 |
|
Introduction to Time Series and Forecasting |
449 Pages |
2002 |
|
Introductory Econometrics |
910 Pages |
2012 |
|
Introductory R Presentation |
41 Pages |
2014 |
 |
Introductory Statistics with R |
370 Pages |
2008 |
|
Introductory Statistics |
641 Pages |
2015 |
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Introductory Time Series with R |
87 Pages |
2006 |
J
|
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Java Data Mining |
545 Pages |
2007 |
K
|
 |
Kalman and Bayesian Filters in Python |
|
215 |
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Kalman and Bayesian Filters in Python |
|
2015 |
 |
Kalman and Bayesian Filters in Python |
456 Pages |
2015 |
|
Kernel Based Algorithms for Mining Huge Data Sets |
267 Pages |
2006 |
L
|
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Latent Dirichlet Allocation in R |
146 Pages |
2012 |
 |
Learn Python the Hard Way |
|
2015 |
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Learn SQL The Hard Way |
|
2010 |
|
Learning Bayesian Networks |
59 Pages |
2011 |
|
Learning Deep Architectures for AI |
130 Pages |
2009 |
 |
Learning Python |
443 Pages |
2018 |
|
Learning with Big Data – The Future of Education |
63 Pages |
2014 |
|
Lecture Notes on Graph Theory |
100 Pages |
2011 |
|
Linear Algebra – CDW |
430 Pages |
2013 |
|
Linear Algebra – Theory And Applications |
504 Pages |
2015 |
|
Linear Algebra and its Applications – Lay |
576 Pages |
2012 |
|
Linear Algebra and its Applications |
508 Pages |
2006 |
|
Linear Algebra Done Wrong |
276 Pages |
2009 |
|
Linear Algebra |
497 Pages |
2014 |
 |
Linear Models with R |
255 Pages |
2005 |
|
LIONBook |
325 Pages |
2014 |
 |
List of 35 Free Online Books on Machine Learning |
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|
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listudy – Free Online Books for Data Science |
|
|
M
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Machine Learning |
450 Pages |
2009 |
|
Machine Learning – A Probabilistic Perspective |
1098 Pages |
2012 |
|
Machine Learning – The Complete Guide |
|
2018 |
|
Machine Learning – The Complete Guide |
|
2015 |
|
Machine Learning Cheat Sheet |
133 Pages |
2014 |
 |
Machine Learning with R |
396 Pages |
2018 |
|
Machine Learning Yearning |
|
2018 |
|
Machine Learning, Neural and Statistical Classification |
298 Pages |
1994 |
|
Machine Learning |
421 Pages |
1997 |
|
Markov Chains and Mixing Times Oregon |
387 Pages |
2008 |
|
Markov Chains and Mixing Times |
269 Pages |
2007 |
|
Mathematical Tools for Data Mining |
610 Pages |
2008 |
|
Mathematics for Computer Science |
848 Pages |
2013 |
 |
MATLAB – Econometrics Toolbox Users Guide |
2012 Pages |
2015 |
|
Matrix Differential Calculus With Applications in Statistics and Econometrics |
468 Pages |
2007 |
|
Matrix Methods and Applications |
123 Pages |
2014 |
 |
Matters Computational |
978 Pages |
2010 |
|
Measuring Inequality |
255 Pages |
2009 |
|
Methods of Multivariate Analysis |
727 Pages |
2002 |
|
Mining of Massive Datasets |
513 Pages |
2014 |
|
Model-Based Machine Learning |
|
2017 |
|
Model-based Machine Learning |
|
2015 |
|
Modeling Agents with Probabilistic Programs |
|
2017 |
 |
Modeling and Solving Linear Programming with R |
108 Pages |
2015 |
 |
Modeling DataWith Functional Programming In R |
195 Pages |
2015 |
|
Modeling with Data |
470 Pages |
2009 |
|
Modern Multivariate Statistical Techniques |
757 Pages |
2008 |
 |
ModernDive |
|
2017 |
|
Multi Sensor Data Fusion |
154 Pages |
2001 |
|
Multiagent Systems |
532 Pages |
2009 |
|
Multivariate Density Estimation |
381 Pages |
2015 |
 |
Multivariate Nonparametric Methods with R |
247 Pages |
2010 |
|
Multivariate Statistics Old School |
342 Pages |
2013 |
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Multivariate Statistics with R |
189 Pages |
2009 |
N
|
 |
Natural Language Processing |
|
2008 |
|
Natural Language Processing and Text Mining |
272 Pages |
2006 |
|
Natural Language Processing for the Working Programmer |
|
2011 |
|
Natural Language Processing for the Working Programmer |
78 Pages |
2011 |
 |
Natural Language Processing with Python |
|
2016 |
 |
Natural Language Processing with Python |
|
2009 |
 |
Natural Language Processing with Python |
504 Pages |
2009 |
|
Network Science Book |
|
2012 |
|
Networks Crowds and Markets |
833 Pages |
2010 |
|
Neural Data Mining with Python Sources |
112 Pages |
2013 |
|
Neural Networks – A Systematic Introduction |
509 Pages |
1996 |
|
Neural Networks and Deep Learning |
|
2014 |
|
Neural Networks for Pattern Recognition |
498 Pages |
1995 |
|
Niching Methods for Genetic Algorithms |
264 Pages |
1995 |
|
Nonparametric Econometrics – A Primer |
88 Pages |
2008 |
|
Numerical Algorithms and Digital Representation |
463 Pages |
2013 |
O
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|
On Intelligence |
174 Pages |
2005 |
|
OpenIntro Advanced High School Statistics |
402 Pages |
2014 |
|
OpenIntro Introductory Statistics with Randomization and Simulation |
354 Pages |
2014 |
|
OpenIntro Statistics V1 |
380 Pages |
2011 |
|
OpenIntro Statistics V2 |
426 Pages |
2014 |
|
open-Source Media Interpretation by Large feature-space Extraction |
173 Pages |
2014 |
|
Optimize Your Operations With Predictive Maintenance |
13 Pages |
2017 |
P
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|
Palgrave Handbook of Econometrics |
1377 Pages |
2009 |
 |
Partial Least Squares Path Modeling with R |
235 Pages |
2013 |
|
Past Present Future of Statistical Science |
643 Pages |
2014 |
|
Pattern Classification |
738 Pages |
2000 |
|
Pattern Recognition and Machine Learning – Chapter 8 – Graphical Models |
82 Pages |
2006 |
|
Pattern Recognition and Machine Learning |
703 Pages |
2006 |
|
Practical Artificial Intelligence in the Cloud |
21 Pages |
2017 |
 |
Practical Artificial Intelligence Programming With Java |
222 Pages |
2008 |
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Practical Data Analysis with Python |
|
2015 |
|
Practical Data Analysis |
360 Pages |
2018 |
|
Practical Machine Learning |
56 Pages |
2014 |
|
Predictive Analytics and Data Mining |
164 Pages |
2013 |
|
Predictive Analytics for Dummies |
50 Pages |
2014 |
 |
Predictive Modeling and Analytics |
488 Pages |
2014 |
|
Predictive Policing: Taking a Chance for a Safer Future |
162 Pages |
2015 |
|
Principles of Data Mining – Bramer |
342 Pages |
2007 |
|
Principles of Data Mining |
322 Pages |
2001 |
|
Principles of Survival Analysis |
128 Pages |
2012 |
|
Principles of Uncertainty |
499 Pages |
2011 |
 |
Pro Data Visualization using R and JavaScript |
207 Pages |
2007 |
|
Probabilistic Graphical Models Principles and Techniques |
1268 Pages |
2009 |
|
Probabilistic Graphical Models |
1265 Pages |
2009 |
 |
Probabilistic Programming & Bayesian Methods for Hackers |
|
2015 |
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Probabilistic Programming & Bayesian Methods for Hackers |
|
2014 |
|
Probability – Theory and Examples |
386 Pages |
2013 |
|
Probability and Random Processes |
529 Pages |
2012 |
|
Probability and Statistics Cookbook |
28 Pages |
2014 |
|
Probability and Stochastic Processes with Applications – Teaching |
382 Pages |
2013 |
|
Probability and Stochastic Processes with Applications |
382 Pages |
2009 |
|
Probability |
575 Pages |
1992 |
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Process Improvement Using Data |
378 Pages |
2015 |
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Processing and Analyzing Financial Data with R |
398 Pages |
2017 |
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Process-Oriented Analysis and Validation of Multi-Agent-Based Simulations |
445 Pages |
2013 |
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Programmieren mit R |
31 Pages |
2012 |
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Programming Collective Intelligence |
360 Pages |
2007 |
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p-value.info – Free Datascience Books |
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Python Algorithms |
337 Pages |
2010 |
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Python for Computational Science and Engineering |
170 Pages |
2016 |
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Python Programming |
|
2015 |
R
|
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R and Data Mining Examples and Case Studies |
160 Pages |
2013 |
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R for beginners |
76 Pages |
2005 |
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R for Data Science |
|
2016 |
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R for Programmers |
104 Pages |
2008 |
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R Graph Cookbook |
272 Pages |
2011 |
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R Graphics Cookbook |
413 Pages |
2012 |
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R in a Nutshell |
636 Pages |
2009 |
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R Packages |
|
2015 |
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R Programming |
|
2015 |
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R Programming for Data Science |
132 Pages |
2015 |
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R Reader |
69 Pages |
2009 |
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Rabbit – Introduction to R |
|
2015 |
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Ramarro – R for Developers |
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2014 |
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Random Forests for Beginners |
71 Pages |
2014 |
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Real-Time Big Data Analytics – Emerging Architecture |
32 Pages |
2013 |
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Real-World Hadoop |
103 Pages |
2015 |
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Recommender Systems Handbook |
845 Pages |
2011 |
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Reinforcement Learning |
434 Pages |
2008 |
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Reinforcement Learning – An Introduction 2012 |
334 Pages |
2006 |
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Reinforcement Learning – An Introduction |
398 Pages |
2005 |
S
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Sampling – Design and Analysis |
609 Pages |
2010 |
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School of Data Handbook |
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2013 |
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Sentic Computing |
177 Pages |
2012 |
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Similarity and Dissimilarity Measures |
61 Pages |
2012 |
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simpleR – Using R for Introductory Statistics |
114 Pages |
2002 |
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Social Media Mining |
382 Pages |
2014 |
|
Soft Computing – Overview and Recent Developments in Fuzzy Optimization |
198 Pages |
2001 |
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Software for Data Analysis |
514 Pages |
2008 |
|
Speech and Language Processing |
975 Pages |
2000 |
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SQL Tutorial |
200 Pages |
|
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Statistical Analysis with R |
47 Pages |
2004 |
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Statistical Analysis with The General Linear Model |
274 Pages |
2013 |
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Statistical Foundations of Machine Learning |
* Pages |
2013 |
|
Statistical Inference – Solutions Manual |
195 Pages |
2002 |
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Statistical Inference for Everyone |
200 Pages |
2014 |
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Statistical Inference |
686 Pages |
2002 |
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Statistical Learning Theory – Models Concepts Results |
40 Pages |
2008 |
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Statistical Learning Theory and Sequential Prediction |
178 Pages |
2013 |
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Statistical Learning Theory |
740 Pages |
1998 |
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Statistical Learning with Similarity and Dissimilarity Functions |
166 Pages |
2004 |
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Statistical Learning with Sparsity |
362 Pages |
2015 |
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Statistics Done Wrong |
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2017 |
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Statistics FlexBook |
308 Pages |
2013 |
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Statistics With R |
121 Pages |
2015 |
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Statistics |
236 Pages |
2012 |
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Statlect: Digital Textbook on Probability and Statistics |
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2015 |
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Stochastic Processes – Theory for Applications |
85 Pages |
2013 |
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Street-Fighting Mathematics |
153 Pages |
2010 |
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Structural Econometric Modeling |
139 Pages |
2007 |
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Summated Rating Scale Construction |
84 Pages |
1992 |
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Survival Analysis – Introduction |
491 Pages |
2005 |
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Survival Analysis – Techniques for Censored and Truncated Data |
542 Pages |
2003 |
T
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Table of Integrals Series and Products |
1221 Pages |
2007 |
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Terms of Service |
48 Pages |
2014 |
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The Algorithm Design Manual |
739 Pages |
2008 |
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The Art and Science of Data Driven Journalism |
145 Pages |
2014 |
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The Art of Data Science |
159 Pages |
2015 |
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The Art of R Programming |
404 Pages |
2011 |
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The Art of Turning Data into Product |
29 Pages |
2014 |
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The Atlas of Economic Complexity |
362 Pages |
2007 |
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The Behavioral Economics Guide 2014 |
131 Pages |
2014 |
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The C++ Programming Language |
1361 Pages |
2013 |
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The Data Analytics Handbook |
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2015 |
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The Data Science Handbook |
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The Data Scientists Guide to Apache Spark |
103 Pages |
2017 |
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The Design of Approximation Algorithms |
500 Pages |
2010 |
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The Elements of Data Analytic Style |
98 Pages |
2015 |
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The Elements of Statistical Learning |
764 Pages |
2013 |
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The Field Guide to Data Science |
110 Pages |
2013 |
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The Handbook of Computational Linguistics and Natural Language Processing |
801 Pages |
2010 |
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The Hitchhikers Guide to GGPlot2 in R |
218 Pages |
2016 |
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The Matrix Cookbook |
72 Pages |
2012 |
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The Media Lab |
329 Pages |
1987 |
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The Nature of Statistical Learning Theory |
314 Pages |
2000 |
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The R Guide |
61 Pages |
2010 |
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The R Inferno |
126 Pages |
2011 |
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The Text Mining Handbook |
423 Pages |
2007 |
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The Top Ten Algorithms in Data Mining |
214 Pages |
2009 |
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The Visual Display Of Quantitative Information |
191 Pages |
2007 |
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Theory and Applications for Advanced Text Mining |
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2012 |
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Theory of Convex Optimization for Machine Learning |
110 Pages |
2014 |
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Theory of Decision under Uncertainty |
279 Pages |
2008 |
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Think Bayes |
213 Pages |
2012 |
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Think Python |
240 Pages |
2012 |
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Think Stats 2 |
242 Pages |
2014 |
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Think Stats |
140 Pages |
2011 |
|
Thinking With Data |
220 Pages |
2013 |
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Time Series Analysis and its Applications |
609 Pages |
2011 |
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Time Series Analysis in R |
138 Pages |
2012 |
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Time Series Analysis with R |
75 Pages |
2011 |
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Time Series Modelling of Water Resources and Environmental Systems |
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1994 |
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Time-Critical Decision Making for Business Administration |
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2015 |
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Twitter Data Analytics |
89 Pages |
2013 |
U
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Understanding Big Data |
166 Pages |
2012 |
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Understanding Machine Learning – From Theory to Algorithms |
449 Pages |
2014 |
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Understanding the Chief Data Officer |
26 Pages |
2015 |
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Using Google Analytics with R |
82 Pages |
2016 |
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Using R for Data Analysis and Graphics |
96 Pages |
2008 |
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Using R for Introductory Statistics |
413 Pages |
2005 |
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Using R for Linear Regression |
9 Pages |
2007 |
V
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Visualizing Data |
384 Pages |
2008 |
W
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Wikibook: Data Mining Algorithms In R |
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Wikibook: Data Science – An Introduction |
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Wikibook: R Programming |
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|
Wikipedia – Machine Learning – The Complete Guide |
1990 Pages |
2014 |
Z
|
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Zeitreihenanalyse mit R |
51 Pages |
2010 |
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