S.No. |
Book Name |
Author(s) |
Pages |
1 |
A Brief Introduction to Machine Learning for Engineers |
Osvaldo Simeone |
206 |
2 |
A Course in Machine Learning |
Hal Daumé |
227 |
3 |
A First Course in Machine Learning – Volume in Machine Learning and Pattern Recognition Series |
Simon Rogers,
Mark Girolami |
307 |
4 |
A Probabilistic Theory of Pattern Recognition |
Luc Devroye,
Laszlo Gyorfi,
Gabor Lugosi |
654 |
5 |
Advanced Data Analytics Using Python – With Machine Learning, Deep Learning and NLP Examples |
Sayan Mukhopadhyay |
195 |
6 |
Advanced Machine Learning with Python |
John Hearty |
278 |
7 |
Advances in Financial Machine Learning |
Marcos Lopez De Prado |
393 |
8 |
Advances in Machine Learning And Data Analysis |
Sio-Iong Ao,
Burghard Rieger,
Mahyar Amouzegar |
241 |
9 |
AI Based Robot Safe Learning And Control |
Xuefeng Zhou,
Zhihao Xu,
Shuai Li,
Hongmin Wu,
Taobo Cheng,
Xiaojing Lv |
138 |
10 |
An Introduction to Machine Learning Intrepretability Second Edition |
Patrick Hall,
Navdeep Gill |
62 |
11 |
An Introduction to Machine Learning |
Miroslav Kubat |
348 |
12 |
An Introduction to Statistical Learning with Applications in R |
Gareth James,
Daniela Witten,
Trevor Hastie,
Robert Tibshirani |
436 |
13 |
Applications of Machine Learning in Wireless Communications |
Ruisi He,
Zhiguo Ding |
492 |
14 |
Applied Deep Learning – A Case-Based Approach to Understanding Deep Neural Networks |
Umberto Michelucci |
425 |
15 |
Applied Natural Language Processing with Python |
Taweh Beysolow |
158 |
16 |
Applied Text Analysis with Python – Enabling Language-Aware Data Products with Machine Learning |
Benjamin Bengfort,
Rebecca Bilbro,
Tony Ojeda |
332 |
17 |
Artificial Intelligence – A Modern Approach – Third Edition |
Stuart J. Russell,
Peter Norvig |
1151 |
18 |
Artificial Intelligence – Foundations of Computational Agents |
David L. Poole,
Alan K.Mackworth |
682 |
19 |
Artificial Intelligence – Third Edition |
Patrick Henry Winston |
640 |
20 |
Artificial Intelligence and Machine Learning Fundamentals |
Zsolt Nagy |
712 |
21 |
Artificial Neural Networks and Machine Learning – ICANN 2016 |
Alessandro E.P. Villa,
Paolo Masulli,
Antonio Javier Pons Rivero |
585 |
22 |
Artificial Neural Networks and Machine Learning – ICANN 2018 |
Vera Kurková,
Yannis Manolopoulos,
Barbara Hammer,
Lazaros Iliadis,
Ilias Maglogiannis |
854 |
23 |
Automated Machine Learning |
Frank Hutter,
Lars Kotthoff,
Joaquin Vanschoren |
222 |
24 |
Azure Machine Learning |
Jeff Barnes |
240 |
25 |
Basics for Linear Algebra for Machine Learning – Discover the Mathematical Language of Data in Python |
Jason Brownlee |
212 |
26 |
Bayesian Reasoning and Machine Learning |
David Barber |
739 |
27 |
Beginning Apache Spark 2 – With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning library |
Hien Luu |
398 |
28 |
Beginning Machine Learning in the Browser Quick-start Guide to Gait Analysis with JavaScript and TensorFlow.js |
Nagender Kumar Suryadevara |
193 |
29 |
Big Data, Artificial Intelligence, Machine Learning and Data Protection |
– |
114 |
30 |
Big Data, Data Mining, and Machine Learning |
Jared Dean |
289 |
31 |
Bioinformatics – The Machine Learning Approach – Second Edition |
Pierre Baldi,
Soren Brunak |
477 |
32 |
Building Chatbots with Python – Using Natural Language Processing and Machine Learning |
Sumit Raj |
205 |
33 |
Building Intelligent Systems – A Guide to Machine Learning Engineering |
Geoff Hulten |
346 |
34 |
Building Machine Learning Systems with Python |
Willi Richert,
Luis Pedro Coelho |
290 |
35 |
Combinatorial Machine Learning – A Rough Set Approach |
Mikhail Moshkov,
Beata Zielosko |
186 |
|
36 |
Computer Age Statistical Inference
|
Bradley Efron,
Trevor Hastie |
493 |
37 |
Computer Programming For Beginners |
Kevin Cooper |
506 |
38 |
Conformal Prediction for Reliable Machine Learning |
Vineeth N. Balasubramanian,
Shen-Shyang Ho,
Vladimir Vovk |
300 |
|
39 |
Convolutional Neural Networks in Python |
The LazyProgrammer |
75 |
40 |
Cyber Security Cryptography and Machine Learning |
Shlomi Dolev,
Sachin Lodha |
318 |
41 |
Data Mining – Practical Machine Learning Tools and Techniques – 2nd Edition |
Ian H. Witten,
Eibe Frank |
558 |
42 |
Data Mining – Practical Machine Learning Tools and Techniques – 3rd Edition |
Ian H. Witten,
Eibe Frank,
Mark A. Hall |
665 |
43 |
Data Mining and Machine Learning in Cyber security |
Sumeet Dua,
Xian Du |
248 |
44 |
Deep learning – Adaptive Computation and Machine Learning |
Ian Goodfellow,
Yoshua Bengio,
Aaron Courville |
801 |
45 |
Deep Learning for Natural Language Processing |
Palash Goyal,
Sumit Pandey,
Karan Jain |
290 |
46 |
Deep Learning in Python |
Lazy Programmer |
104 |
47 |
Deep Learning Interviews |
SHLOMO KASHANI |
401 |
48 |
Deep Learning with PyTorch |
Eli Stevens,
Luca Antiga |
141 |
49 |
Deep Thinking – Where Machine Intelligence Ends and Human Creativity Begins. |
Garry Kasparov |
230 |
50 |
Designing Machine Learning Systems with Python |
David Julian |
232 |
51 |
Efficient Learning Machines – Theories, Concepts, and Applications for Engineers and System Designers |
Mariette Awad,
Rahul Khanna |
263 |
52 |
Ensembles in Machine Learning Applications |
Oleg Okun,
Giorgio Valentini,
Matteo Re |
262 |
53 |
Extreme Learning Machines 2013 – Algorithms and Applications |
Fuchen Sun,
Kar-Ann Toh,
Manuel Grana Romay,
Kezhi Mao |
224 |
54 |
Feature Engineering for Machine Learning – Principles and Techniques for Data Scientists |
Alice Zheng,
Amanda Casari |
217 |
55 |
Financial Signal Processing and Machine Learning |
Ali N. Akansu,
Sanjeev R. Kulkarni,
Dmitry MalioutovI |
440 |
56 |
Foundations of Machine Learning – Second Edition |
Mehryar Mohri,
Afshin Rostamizadeh,
Ameet Talwalkar |
505 |
57 |
Foundations of Machine Learning |
Mehryar Mohri,
Afshin Rostamizadeh,
Ameet Talwalkar |
427 |
58 |
From Curve Fitting to Machine Learning. |
Achim Zielesny |
476 |
59 |
Fundamentals of Machine Learning for Predictive Data Analytics |
John D. Kelleher,
Brian Mac Namee,
Aoife D’Arcy |
691 |
60 |
Gaussian Processes for Machine Learning |
Carl Edward Rasmussen,
Christopher K. I. Williams |
266 |
61 |
Genetic Algorithms and Machine Learning for Programmers – Create AI Models and Evolve Solution |
Frances Buontempo |
234 |
62 |
Growing Adaptive Machines – Combining Development and Learning in Artificial Neural Networks |
Taras Kowaliw,
Nicolas Bredeche,
René Doursat |
266 |
63 |
Hackers Guide to Machine Learning with Python |
Venelin Valkov |
269 |
64 |
Hands-On Machine Learning with Scikit-Learn and TensorFlow – Concepts, Tools, and Techniques TO BUILD INTELLIGENT SYSTEMS |
Aurélien Géron |
564 |
65 |
Hands–On Machine Learning with Scikit–Learn and TensorFlow – 2nd Edition |
Aurélien Géron |
510 |
66 |
Human and Machine Consciousness |
David Gamez |
236 |
67 |
Identifying Product and Process State Drivers in Manufacturing Systems using Supervised Machin |
Thorsten Wuest |
284 |
68 |
Introducing Data Science – Big Data, Machine Learning, and more, using Python tools |
Davy Cielen,
Arno D. B. Meysman,
Mohamed Ali |
322 |
69 |
Introduction to Deep Learning Using R – A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R |
Taweh Beysolow |
240 |
70 |
Introduction to Machine Learning – Second Edition – Adaptive Computation and Machine Learning |
Ethem Alpaydın |
581 |
71 |
Introduction to Machine Learning – Second Edition |
Ethem Alpaydm |
564 |
72 |
Introduction To Machine Learning – Yves Kodratoff |
Yves Kodratoff |
302 |
73 |
Introduction to Machine Learning with Python |
Andreas C. Müller,
Sarah Guido |
392 |
74 |
Introduction to Machine Learning |
Ethem Alpaydın |
639 |
75 |
Introduction to Pattern Recognition and Machine Learning |
M Narasimha,
Der V Sunshield Devi |
402 |
76 |
Introduction to Probability |
Joseph K. Blitzstein,
Jessica Hwang |
589 |
77 |
Keras to KubernetesThe Journey of a Machine Learning Model to Production |
Dattaraj Jagdish Rao |
313 |
78 |
Kernel Methods and Machine Learning |
S. Y. KUNG |
603 |
79 |
Kernel-Based Data Fusion for Machine Learning – Methods and Applications in Bioinformatics and Text Mining |
Shi Yu,
Léon-Charles Tranche vent,
Bart De Moor,
Yves Moreau |
228 |
80 |
Large Scale Machine Learning with Python |
Bastiaan Sjardin,
Luca Massaron,
Alberto Boschetti |
420 |
81 |
Large Scale Machine Learning with Spark |
Md. Rezaul Karim,
Md. Mahedi Kaysar |
472 |
82 |
Learn Keras for Deep Neural Networks – A Fast-Track Approach to Modern Deep Learning with Python |
Jojo Moolayil |
192 |
83 |
Learning And Soft Computing – Support Vector Machines, Neural Networks, And Fuzzy Logic Models |
Vojislav Kecman |
576 |
84 |
Learning NumPy Array |
Ivan Idris |
164 |
85 |
Learning with Kernels |
Bernhard Scholkopf,
Alexander J. Smola |
645 |
86 |
Linear Algebra |
Jim Hefferon |
525 |
87 |
Machine Learning – A Bayesian and Optimization Perspective |
Sergios Theodoridis |
1046 |
88 |
Machine Learning – A Probabilistic Perspective |
Kevin P. Murphy |
1098 |
89 |
Machine Learning – Algorithms and Applications |
Mohssen Mohammed,
Muhammad Badruddin Khan,
Eihab Bashier Mohammed Bashier |
213 |
90 |
Machine Learning – An Algorithmic Perspective – 2nd Edition |
Stephen Marsland |
452 |
91 |
Machine learning – An Algorithmic Perspective |
Stephen Marsland |
408 |
92 |
Machine Learning – An Applied Mathematics Introduction |
Paul Wilmot |
246 |
93 |
Machine Learning – Hands-On for Developers and Technical Professionals |
Jason Bell |
407 |
94 |
Machine Learning – Step By Step Guide To Implement Machine Learning Algorithms with Python |
Rudolph Russell |
103 |
95 |
Machine Learning – The Art and Science of Algorithms that Make Sense of Data |
PETER FLACH |
416 |
96 |
Machine Learning – The New AI |
ETHEM ALPAYDIN |
225 |
97 |
Machine Learning – Tom Mitchell |
Tom M. Mitchell |
421 |
98 |
Machine Learning and AI for Healthcare Big Data for Improved Health Outcomes |
Arjun Panesar |
390 |
99 |
Machine Learning And Its Applications |
Georgios Paliouras,
Vangelis Karkaletsis,
Constantine D Spyropoulos |
334 |
100 |
Machine Learning and Security – Protecting Systems with Data and Algorithms |
Clarence Chio,
David Freeman |
385 |
101 |
Machine Learning and Systems Engineering |
Sio-Iong Ao,
Burghard Rieger,
Mahyar A. Amouzegar |
635 |
102 |
Machine Learning Applications Using Python – Cases Studies from Healthcare, Retail, and Finance |
Puneet Mathur |
384 |
103 |
Machine Learning by Tutorials – Beginning Machine Learning for Apple and iOS |
Matthijs Hollemans,
Chris LaPollo,
Audrey Tam |
250 |
104 |
Machine Learning for Absolute Beginners |
Oliver Theobald |
128 |
105 |
Machine Learning for Developers |
Rodolfo Bonnin |
234 |
106 |
Machine Learning for Dummies – IBM |
Judith Hurwitz,
Daniel Kirsch |
75 |
107 |
Machine Learning For Dummies |
John Paul Mueller,
Luca Massaron |
435 |
108 |
Machine Learning for FinanceA Systematic Approach to Predictive Risk and Performance Analysis for Investment Portfolio |
Tshepo Chris Nokeri |
192 |
109 |
Machine Learning for Hackers |
Drew Conway,
John Myles White |
322 |
110 |
Machine Learning for Humans |
Vishal Maini,
Samer Sabri |
97 |
111 |
Machine Learning for the Web |
Andrea Isoni |
298 |
112 |
Machine Learning Hands-On for Developers and Technical Professionals |
Jason Bell |
407 |
113 |
Machine Learning in Action |
Peter Harrington |
382 |
114 |
Machine Learning in Computer Vision |
N.Sebe,
Ira Cohen,
Ashutosh Garg,
Thomas S. Huang |
249 |
115 |
Machine Learning in Java |
Bostjan Kaluza |
258 |
116 |
Machine Learning in Medicine – A Complete Overview |
Ton J. Cleophas,
Aeilko H. Zwinderman |
498 |
117 |
Machine Learning in Python – Essential Techniques for Predictive Analysis |
Michael Bowles |
367 |
118 |
Machine Learning in VLSI Computer-Aided Design |
Ibrahim (Abe) M. Elfadel,
Duane S. Boning,
Xin Li |
697 |
119 |
Machine Learning Mastery With Python Understand Your Data, Create Accurate Models and Work Projects End-To-End |
Jason Brownlee |
179 |
120 |
Machine Learning Models and Algorithms for Big Data Classification – Thinking with Examples for Effective Learning |
Shan Suthaharan |
364 |
121 |
Machine Learning Paradigms – Applications in Recommender Systems |
Aristomenis S. Lampropoulos,
George A. Tsihrintzis |
135 |
122 |
Machine Learning Projects for .NET Developer |
Mathias Brandewinder |
390 |
123 |
Machine Learning Projects with Python |
Brian Hogan,
Mark Drake |
135 |
124 |
Machine Learning Refined – Foundations, Algorithms, and Applications |
Jeremy Watt,
Reza Borhani,
Aggelos K. Katsaggelos |
301 |
125 |
Machine Learning Using C# Succinctly |
James McCaffrey |
148 |
126 |
Machine Learning with Spark |
Nick Pentreath |
338 |
127 |
Machine Learning with TensorFlow |
Nishant Shukla,
Kenneth Fricklas |
274 |
128 |
Machine Trading – Deploying Computer Algorithms to Conquer the Markets |
Ernest P. Chan |
267 |
129 |
Machines and Mechanisms – Applied Kinematic Analysis |
David H. Myszka |
385 |
130 |
Master Machine Learning Algorithms – Discover how they work |
Jason Brownlee |
163 |
131 |
Mastering Machine Learning with Python in Six Steps – A Practical Implementation Guide to Predictive Data Analytics Using Python |
Manohar Swamynathan |
374 |
132 |
Mastering Scala Machine Learning |
Alex Kozlov |
310 |
133 |
Mathematics For Machine Learning |
Marc Peter Deisenroth,
A. Aldo Faisal,
Cheng Soon Ong |
412 |
134 |
MATLAB Deep Learning With Machine Learning, Neural Networks and Artificial Intelligence |
Phil Kim |
162 |
135 |
MATLAB Machine Learning Recipes – A Problem-Solution Approach |
Michael Paluszek,
Stephanie Thomas |
358 |
136 |
MATLAB Machine Learning |
Michael Paluszek,
Stephanie Thomas |
335 |
137 |
Microsoft Azure Machine Learning |
Sumit Mund |
212 |
138 |
Monetizing Machine Learning Quickly Turn Python ML Ideas Into Web Applications on the Serverlss Cloud |
Manuel Amunategui,
Mehdi Roopaei |
510 |
139 |
Natural Language Annotation for Machine Learning |
James Pustejovsky,
Amber Stubbs |
343 |
140 |
Natural Language Processing Recipes – Unlocking Text Data with Machine Learning and Deep Learning using Python |
Akshay Kulkarni,
Adarsha Shivananda |
253 |
141 |
Neo4j – Graph Algorithms |
Mark Needham,
Amy E. Hodler |
257 |
142 |
Neural Network Design |
Martin T.Hagan,
Howard B. Demuth,
Mark Hudson Beale,
Orlando De Jesús |
1012 |
143 |
Neural Network Programming with Java |
Fábio M. Soares,
Alan M.F. Souza |
244 |
144 |
Neural Networks Using C# Succinctly |
James McCaffrey |
128 |
145 |
Neural Networks with JavaScript Succinctly |
James McCaffrey |
163 |
146 |
Neural Networks |
DavidKriesel |
242 |
147 |
Numerical Algorithms – Methods for Computer Vision, Machine Learning, and Graphics |
Justin Solomon |
392 |
148 |
Numerical Modelling and Design of Electrical Machines and Devices |
K. Hameyer,
R. Belmans |
340 |
149 |
Optimization for Machine Learning |
Suvrit Sra,
Sebastian Nowozin,
Stephen J. Wright |
509 |
150 |
Pattern Recognition and Machine Learning |
Christopher M. Bishop |
758 |
151 |
Patterns, Predictions, and Actions – A Story About Machine Learning |
Moritz Hardt,
Benjamin Recht |
309 |
152 |
Physics Based Deep Learning |
N. Thuerey,
P. Holl, M. Mueller,
P. Schnell,
F. Trost,
K. Um |
287 |
153 |
Planning Algorithms |
Steven M. LaValle |
512 |
154 |
Practical Artificial Intelligence – Machine Learning, Bots, and Agent Solutions Using C# |
Arnaldo Perez Castano |
701 |
155 |
Practical Guide to Cluster Analysis in R – Unsupervised Machine Learning |
Alboukadel Kassambara |
187 |
156 |
Practical Java Machine Learning – Projects with Google Cloud Platform and Amazon Web Services |
Mark Wickham |
410 |
157 |
Practical Machine Learning and Image Processing For Facial Recognition, Object Detection, and Pattern Recognition Using Python |
Himanshu Singh |
177 |
158 |
Practical Machine Learning with H2O |
Darren Cook |
374 |
159 |
Practical Machine Learning with Python Problem-Solver’s Guide to Building Real-World Intelligent Systems |
Dipanjan Sarkar,
Raghav Bali |
545 |
160 |
Practical Machine Learning |
Sunila Gollapudi |
468 |
|
161 |
Principles and Theory for Data Mining and Machine Learning |
Bertrand Clarke,
Ernest Fokoue,
Hao Helen Zhang |
793 |
162 |
Pro Machine Learning Algorithms A Hands-On Approach to Implementing Algorithms in Python and R |
V Kishore Ayyadevara |
379 |
163 |
Probability for Statistics and Machine Learning – Fundamentals and Advanced Topics |
Anirban DasGupta |
|
164 |
Python – Deeper Insights into Machine Learning Leverage benefits of machine learning techniques using Python |
Sebastian Raschka,
David Julian,
John Hearty |
901 |
165 |
Python for Probability, Statistics, and Machine Learning |
Jose Unpingco |
288 |
166 |
Python Machine Learning – A Step-by-Step Guide |
Konnor Cluster |
90 |
167 |
Python Machine Learning – WEI-MENG LEE |
Wei-Meng Lee |
307 |
168 |
Python Machine Learning Blueprints |
Alexander T. Combs |
324 |
169 |
Python Machine Learning Case Studies – Five Case Studies for the Data Scientist |
Danish Haroon |
216 |
170 |
Python Machine Learning Cookbook – Early Release |
Chris Alban |
|
171 |
Python Machine Learning Cookbook |
Prateek Joshi |
304 |
172 |
Python Machine Learning |
Sebastian Raschka |
|
173 |
Quantum Machine Learning – What Quantum Computing Means to Data Mining |
Peter Wittek |
176 |
174 |
Recurrent Neural Networks |
Lazy programmer |
139 |
175 |
Reinforcement Learning – With Open AI, TensorFlow and Keras Using Python |
Abhishek Nandy,
Manisha Biswas |
174 |
176 |
Scala for Machine Learning – Leverage Scala and Machine Learning to construct and study systems that can learn from data |
Patrick R. Nicolas |
520 |
177 |
Cikit-learn Cookbook – Second Edition |
julian Avila,
Trent Hauck |
368 |
178 |
Sewing For Beginners – Quick & Easy Way To Learn How To Sew With 50 Patterns for Beginners |
Kitty Moore |
130 |
179 |
Signal Processing and Machine Learning for Brain-Machine Interfaces |
Toshihisa Tanaka,
Mahnaz Arvaneh |
356 |
180 |
Soft Computing and Machine Learning with Python |
Zoran Gacovski |
380 |
181 |
Statistical and Machine-Learning Data Mining – Third Edition |
Bruce Ratner |
691 |
182 |
Statistical Regression and Classification – From Linear Models to Machine Learning |
Norman Matloff |
532 |
183 |
Statistical Reinforcement Learning – Modern Machine Learning Approaches |
Ralf Herbrich,
Thore Graepel |
206 |
184 |
Support Vector Machines Succinctly |
Alexandre Kowalczyk |
114 |
185 |
TensorFlow Roadmap |
Amirsina Torfi |
22 |
186 |
The AI Ladder |
Rob Thomas |
27 |
187 |
The Deep Learning Revolution – Machine Intelligence Meets Human Intelligence |
Terrence J. Sejnowski |
354 |
188 |
The Elements of Statistical Learning – Data Mining, Inference, and Prediction, Second Edition |
Trevor Hastie,
Robert Tibshirani,
Jerome Friedman |
764 |
189 |
The Essential Handbook For Leaders |
Marcus Wallenberg |
59 |
190 |
The Lion Way – Machine Learning plus Intelligent Optimization |
Roberto Battiti,
Mauro Brunato |
516 |
191 |
The Master Algorithm – How the Quest for the Ultimate Learning Machine Will Remake Our World |
Pedro Domingos |
322 |
192 |
The Sewing Machine Classroom – Learn the Ins and Outs of Your Machine |
Charlene Phillips |
261 |
193 |
Thoughtful Machine Learning – A Test-Driven Approach |
Matthew Kirk |
235 |
194 |
Thoughtful Machine Learning with Python – A Test-Driven Approach |
Matthew Kirk |
216 |
195 |
Understanding Machine Learning – From Theory to Algorithms |
Shai Shalev-Shwartz,
Shai Ben-David |
416 |
196 |
Unsupervised Deep Learning |
Lazy programmer |
100 |
197 |
Unsupervised Machine Learning in Python |
The Lazy Programmer |
89 |
198 |
Using Python to Develop Analytics, Control and Machine Learning Products |
Zhaoyang Wan |
109 |
199 |
What You Need to Know about Machine Learning |
Gabriel Canepa |
50 |
200 |
What You Need to Know about R |
Raghav Bali,
Dipanjan Sarkar |
62 |