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Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. Machine learning is a branch of artificial intelligence that automates the building of systems that learn from data, identify patterns, and make decisions – with minimal human intervention.
A few examples of Machine Learning are: Recommendation applications, Fraud detection, Predictive maintenance, Text analytics and self driving cars.
Machine learning is a broad and fascinating field. Even today, machine learning technology runs a substantial part of your life, often without you knowing it. Any plausible approach to artificial intelligence must involve learning, at some level, if for no other reason than it’s hard to call a system intelligent if it cannot learn.
Machine learning is also fascinating in its own right for the philosophical questions it raises about what it means to learn and succeed at tasks.
Machine learning is also a very broad field, and attempting to cover everything would be a pedagogical disaster. It is also so quickly moving that any book that attempts to cover the latest developments will be outdated before it gets online.
Below is a list of free Machine Learning E-books available for FREE DOWNLOAD curated from different sources. Hope, you will find them useful in preparing for Machine Learning and Artificial Intelligence.
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 |
Online Machine Learning Resources –
- Advanced R
- R For Data Science
- Machine Learning, Neural and Statistical Classification
- A Course in Machine Learning
- Gaussian Processes for Machine Learning
- Think Bayes 2
- The Hundred-Page Machine Learning Book
- Machine Learning and Big Data
- Artificial Intelligence through Prolog by Neil C. Rowe
- Machine Learning from Scratch
- Computers and Thought: A practical Introduction to Artificial Intelligence
- Paradigms of Artificial Intelligence Programming
- Tidy Modeling with R
- Explanatory Model Analysis
- Hands-On Machine Learning with R
- Model Based Machine Learning
- Introduction to Machine Learning Interviews Book
- Feature Engineering and Selection: A Practical Approach for Predictive Models
- Natural Language Processing with Python
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