| The Master Algorithm |
Pedro Domingos |
2015 |
Basic Books |
978-0465065707 |
Explores the concept of a universal algorithm that can explain all of machine learning. |
| Artificial Intelligence: A Guide to Intelligent Systems |
Michael Negnevitsky |
2005 |
Addison-Wesley |
978-0321194943 |
Provides a comprehensive introduction to AI and machine learning concepts. |
| Deep Learning for the People |
Jürgen Schmidhuber |
2021 |
Self-published |
NaN |
A non-technical overview of deep learning concepts and their applications. |
| Machine Learning for Dummies |
Judith Hurwitz, et al. |
2013 |
Wiley |
978-1118490320 |
A simplified guide to understanding machine learning tools and techniques. |
| Data Science for Business |
Foster Provost, Tom Fawcett |
2013 |
O'Reilly Media |
978-1449363880 |
Explains the principles of data science and its significance in business contexts. |
| Machine Learning Yearning |
Andrew Ng |
2018 |
Self-published |
NaN |
A book on how to structure machine learning projects effectively. |
| The Hundred-Page Machine Learning Book |
Andriy Burkov |
2019 |
Self-published |
978-1999579558 |
A concise guide to understanding the fundamentals of machine learning. |
| Prediction Machines |
Ajay Agrawal, Joshua Gans, Avi Goldfarb |
2018 |
Harvard Business Review Press |
978-1633695673 |
Discusses how AI and machine learning impact business decisions and practices. |
| Hello World: Being Human in the Age of Algorithms |
Hannah Fry |
2018 |
W.W. Norton & Company |
978-0393652746 |
Explores the relationship between humans and algorithms in everyday life. |
| Our Final Invention: Artificial Intelligence and the End of the Human Era |
James Barrat |
2013 |
St. Martin's Press |
978-1250023999 |
A cautionary tale about the future of AI and its implications for humanity. |
| AI Superpowers: China, Silicon Valley, and the New World Order |
Kai-Fu Lee |
2018 |
Houghton Mifflin Harcourt |
978-1328545862 |
Analyzes global AI developments and their impact on the economy and society. |
| The Art of Statistics: Learning from Data |
David Spiegelhalter |
2019 |
Basic Books |
978-1541618510 |
A non-technical introduction to using statistics to draw insights from data. |
| The Book of Why: The New Science of Cause and Effect |
Judea Pearl, Dana Mackenzie |
2018 |
Basic Books |
978-0465097600 |
Explains causal reasoning in data science and its significance. |
| Deep Learning: A Practitioner's Approach |
Adam Gibson, Josh Patterson |
2017 |
O'Reilly Media |
978-1492032649 |
An introduction to deep learning for practical applications. |
| The AI Advantage: How to Put the Artificial Intelligence Revolution to Work |
Thomas H. Davenport |
2018 |
MIT Press |
978-0262039920 |
Focuses on how businesses can leverage AI to gain competitive advantage. |
| The Sentient Machine: The Coming Age of Artificial Intelligence |
Jerry Kaplan |
2015 |
HarperBusiness |
978-0062383395 |
Explores the implications and challenges posed by advanced AI. |
| Machine Learning: An Applied Approach |
Ethem Alpaydin |
2010 |
MIT Press |
978-0262034956 |
An applied perspective on machine learning with practical examples. |
| Automate This: How Algorithms Came to Rule Our World |
Christopher Steiner |
2012 |
Portfolio Hardcover |
978-1591845534 |
Discusses the significance of algorithms in various industries. |
| Factfulness: Ten Reasons We're Wrong About the World—and Why Things Are Better Than You Think |
Hans Rosling, Anna Rosling Rönnlund, Ola Rosling |
2018 |
Flatiron Books |
978-1250107817 |
A guide on critical thinking and understanding global trends through data. |
| Introduction to Machine Learning |
Ethem Alpaydin |
2010 |
MIT Press |
978-0262033614 |
Covers fundamental concepts in machine learning suitable for all readers. |
| The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling |
Ralph Kimball, Margy Ross |
2013 |
Wiley |
978-1119472086 |
A primer on data warehousing and how to understand data relationships. |
| Machine Learning For Absolute Beginners: A Plain English Introduction |
Oliver Theobald |
2018 |
Pipala Publishing |
978-1912445023 |
A very beginner-friendly introduction to the basics of machine learning. |
| Data Science from Scratch: First Principles with Python |
Joel Grus |
2019 |
O'Reilly Media |
978-1492041139 |
Introduces data science concepts and Python programming in a straightforward manner. |
| Deep Learning for Coders with Fastai and PyTorch |
Jeremy Howard, Sylvain Gugger |
2020 |
O'Reilly Media |
978-1492042358 |
Teaches deep learning through code with a focus on practical applications. |
| Data Science for Dummies |
Anil Maheshwari |
2018 |
Wiley |
978-1119571889 |
A basic overview of data science concepts tailored for beginners. |
| Artificial Intelligence: A Guide to Intelligent Systems |
Michael Negnevitsky |
2011 |
Pearson |
978-0136060822 |
A comprehensive introduction to AI tailored for non-specialists. |
| Machine Learning: The New AI |
Ethem Alpaydin |
2016 |
MIT Press |
978-0262035618 |
Offers insights into contemporary advancements in machine learning. |
| Artificial Intelligence: A Very Short Introduction |
Margaret A. Boden |
2018 |
Oxford University Press |
978-0198744481 |
A concise overview of AI, its applications, and implications. |
| Deep Learning with Python |
Francois Chollet |
2017 |
Manning Publications |
978-1617294433 |
An accessible introduction to deep learning with Python. |
| Machine Learning: A Probabilistic Perspective |
Kevin P. Murphy |
2012 |
MIT Press |
978-0262018024 |
Discusses the connections between probability, statistics, and machine learning. |
| Data Science for Executives: Leveraging Machine Intelligence to Drive Business ROI |
Nir Kaldero |
2019 |
Gartner |
978-1947282432 |
Explains how executives can leverage data science for strategic decision-making. |
| Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow |
Aurélien Géron |
2019 |
O'Reilly Media |
978-1492032649 |
A practical guide to machine learning with Python libraries. |
| AI: A Very Short Introduction |
Margaret A. Boden |
2018 |
Oxford University Press |
978-0198744481 |
A succinct introduction to artificial intelligence and its impact. |
| Life 3.0: Being Human in the Age of Artificial Intelligence |
Max Tegmark |
2017 |
Knopf |
978-1101946596 |
Explores how AI may shape the future of life on Earth. |
| Wired for War: The Robotics Revolution and Conflict in the 21st Century |
P.W. Singer |
2009 |
Penguin Press |
978-1594201519 |
Explores the implications of robotics and AI in warfare. |
| AI: The Tumultuous History of the Search for Artificial Intelligence |
Daniel Crevier |
1993 |
Basic Books |
978-0465014087 |
A historical overview of AI development and its challenges. |
| Beyond the Hype: The Ethics of AI and Machine Learning |
Nick Bostrom |
2020 |
Oxford University Press |
978-0198844511 |
Examines the ethical implications of AI technologies. |
| Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking |
Foster Provost, Tom Fawcett |
2013 |
O'Reilly Media |
978-1449363880 |
Discusses how to turn data into actionable business insights. |
| Competing in the Age of AI: How Machine Intelligence Will Transform the Way You Work and Live |
Marco Iansiti, Karim R. Lakhani |
2020 |
Harvard Business Review Press |
978-1633697622 |
Explains how AI changes the way businesses operate. |
| Whydunit? A Handbook for Data Science Enthusiasts |
Melanie D. L. Pique, Vijay R. Rao |
2016 |
Morgan Kaufmann |
978-0128020461 |
Introduction to the principles of causality in data science. |
| Data Science for the Social Good: How Data Can Help Solve Humanitarian Issues |
Catherine D. M. O'Reilly |
2020 |
Imperial College Press |
978-1786347249 |
Explains how data science can tackle social challenges. |
| AI Ethics: A New Perspective on the Relationship between Technology and Society |
Elissa M. Perry |
2021 |
MIT Press |
978-0262046665 |
Explores ethical considerations in AI and technology. |
| Mathematics for Machine Learning |
Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong |
2020 |
Cambridge University Press |
978-1108489245 |
Mathematical foundations for understanding machine learning. |
| The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling |
Ralph Kimball |
2016 |
Wiley |
978-1119470600 |
Comprehensive guide on data warehouse design and dimensional modeling. |
| Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy |
Cathy O'Neil |
2016 |
Crown Publishing Group |
978-0553418811 |
Critiques the impact of algorithms on social justice and democracy. |
| AI Ethics: A Guide to the Ethical Use of Artificial Intelligence |
Ursula Kelly |
2020 |
Springer |
978-9811574743 |
Comprehensive guide on ethical practices in AI technology. |
| Big Data: A Revolution That Will Transform How We Live, Work, and Think |
Viktor Mayer-Schönberger, Kenneth Cukier |
2013 |
Eamon Dolan Books |
978-0544227744 |
A guide on the impact of big data on various sectors. |
| Machine Learning: A Probabilistic Perspective |
Kevin Murphy |
2012 |
MIT Press |
978-0262033614 |
Comprehensive coverage of machine learning methodologies and their probabilistic foundations. |
| Neural Networks and Deep Learning: A Textbook |
Charu C. Aggarwal |
2018 |
Springer |
978-3319994227 |
A non-technical introduction to neural network concepts and applications. |
| Deep Reinforcement Learning Hands-On |
Maxim Lapan |
2018 |
Packt Publishing |
978-1838821134 |
Practical guide to implementing reinforcement learning algorithms. |
| Artificial Intelligence Basics: A Non-Technical Introduction |
Evan A. P. McElroy |
2019 |
Springer |
978-3030040458 |
A beginner's introduction to the concepts of AI without technical jargon. |
| Machine Learning for Kids: A Project-Based Introduction to Artificial Intelligence |
Joshua J. M. Doss |
2018 |
Make Community, LLC |
978-1680453375 |
A hands-on introduction to machine learning through projects. |
| Machine Learning: The New AI |
Ethem Alpaydin |
2016 |
MIT Press |
978-0262035618 |
An accessible overview of machine learning for novices. |
| AI for Everyone |
Andrew Ng |
2019 |
Coursera |
NaN |
An online course providing a non-technical introduction to AI concepts. |
| The Ethics of Artificial Intelligence and Robotics |
Vincent C. Müller |
2020 |
Springer |
978-3030787887 |
Explores ethical issues concerning AI and robotics. |
| Data Science at the Command Line: Facing the Future of Data Analysis |
Jengyee Ho |
2018 |
O'Reilly Media |
978-1491927371 |
Introduction to data science using command line tools. |
| Machine Learning for Absolute Beginners: A Plain English Introduction |
Oliver Theobald |
2018 |
Pipala Publishing |
978-1912445023 |
An easy-to-read guide for beginners looking to understand machine learning. |
| Introduction to Data Mining |
Pang-Ning Tan, Michael Steinbach, Vipin Kumar |
2018 |
Pearson |
978-0133751210 |
Comprehensive introduction to data mining concepts and techniques. |
| Python for Data Analysis |
Wes McKinney |
2017 |
O'Reilly Media |
978-1491957668 |
Focuses on data analysis fundamentals using Python. |
| Machine Learning and Data Science: A Practical Approach via Python |
Sailesh Kumar, Ganesh Kothapalli |
2020 |
Springer |
978-3030242119 |
Provides an approachable guide to machine learning using Python. |
| Data Science for Dummies |
Anil Maheshwari |
2015 |
Wiley |
978-1119283563 |
A simple introduction to data science fundamentals for any reader. |
| AI for People: How AI Is Transforming Our World |
Silvia M. Barabás |
2019 |
The MIT Press |
978-0262037476 |
Explores how AI technologies are shaping human experience. |
| Human Compatible: Artificial Intelligence and the Problem of Control |
Stuart Russell |
2019 |
Viking |
978-0143130921 |
Discusses the challenges of controlling advanced AI systems. |
| JavaScript for Data Science |
Dylan Beattie |
2018 |
Apress |
978-1484230345 |
Introduces JavaScript for data science applications. |
| Machine Learning with R: Expert Techniques for Predictive Modeling |
Brett Lantz |
2015 |
Packt Publishing |
978-1783985205 |
Guides readers through machine learning techniques using R. |
| The Elements of Statistical Learning: Data Mining, Inference, and Prediction |
Trevor Hastie, Robert Tibshirani, Jerome Friedman |
2009 |
Springer |
978-0387848570 |
Advanced statistical methods with machine learning focus. |
| Data Science for the Curious: A Beginner's Guide |
David M. Bradley |
2018 |
Springer |
978-3030045071 |
Explains data science concepts and analytics in a non-technical manner. |
| Machine Learning with Python for Everyone |
Mark E. Fenner |
2019 |
Pearson |
978-0135614289 |
An accessible guide to understanding machine learning with Python. |
| R for Data Science: Import, Tidy, Transform, Visualize, and Model Data |
Hadley Wickham, Garrett Grolemund |
2017 |
O'Reilly Media |
978-1491910397 |
Teaches data science concepts using the R programming language. |
| Artificial Intelligence: Foundations of Computational Agents |
David L. Poole, Alan K. Mackworth |
2010 |
Cambridge University Press |
978-0521513338 |
Comprehensive coverage of AI fundamentals and concepts. |
| Introduction to AI and Data Science in Python |
Ravi Kumar |
2020 |
Springer |
978-3030326940 |
Simplifies AI and data science concepts for beginners using Python. |
| Practical Statistics for Data Scientists: 50 Essential Concepts |
Peter Bruce, Andrew Bruce |
2016 |
O'Reilly Media |
978-1491952960 |
Essential statistical concepts for effective data science practice. |
| Deep Learning with R |
François Chollet, J. J. Allaire |
2018 |
Manning Publications |
978-1617294426 |
Focuses on deep learning using R programming. |
| Artificial Intelligence: A Guide for Thinking Humans |
Melanie Mitchell |
2019 |
Farrar, Straus and Giroux |
978-0374257835 |
A non-specialist overview of AI's capabilities and limitations. |
| Data Science from Scratch: First Principles with Python |
Joel Grus |
2019 |
O'Reilly Media |
978-1492041139 |
Introduces fundamental data science concepts with Python examples. |
| Introduction to Machine Learning in Python |
Andreas C. Müller, Sarah Guido |
2016 |
O'Reilly Media |
978-1449369882 |
A practical introduction to machine learning using Python libraries. |
| AI for Everyone: Master the Future of Work |
Andrew Ng |
2019 |
Coursera |
NaN |
An online course designed for non-technical individuals to understand AI. |
| Machine Learning for Absolute Beginners: A Plain English Introduction |
Oliver Theobald |
2018 |
Pipala Publishing |
978-1912445023 |
An easy entry-level introduction to machine learning concepts. |
| Python Machine Learning |
Sebastian Raschka, Vahid Mirjalili |
2019 |
Packt Publishing |
978-1838982207 |
Focuses on practical aspects of machine learning with Python. |
| Applied Machine Learning: A Practice-Based Approach |
Shiv K. Goyal |
2020 |
Apress |
978-1484251008 |
Hands-on guide for applying machine learning in real-world scenarios. |
| Hands-On Data Science for Python Developers |
Jason Brownlee |
2018 |
Machine Learning Mastery |
978-0994600304 |
Explores data science techniques and their implementations in Python. |
| Data Science for the Curious: An Overview of Concepts and Techniques |
David M. Bradley |
2020 |
Springer |
978-3030045090 |
A beginner-friendly guide to data science terminology and techniques. |
| Linear Algebra and Learning from Data |
Gilbert Strang |
2019 |
Wellesley-Cambridge Press |
978-0980232775 |
Introduces linear algebra concepts with applications in data science and machine learning. |
| Machine Learning: A Probabilistic Perspective |
Kevin P. Murphy |
2012 |
MIT Press |
978-0262033614 |
Detailed treatment of probabilistic techniques in machine learning. |
| Artificial Intelligence: Perspectives and Challenges |
Aruna Rajagopal, Arvind Kumar |
2020 |
Amazon Digital Services LLC |
NaN |
Discusses various aspects and challenges in the field of AI. |
| Think Stats: Statistical Inference for Data Science |
Allen B. Downey |
2011 |
O'Reilly Media |
978-1449392681 |
An introductory text on statistics geared towards data science. |
| Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow |
Aurélien Géron |
2019 |
O'Reilly Media |
978-1492032649 |
An accessible, practical guide to machine learning using popular Python libraries. |
| The Data Science Handbook: A Guide to Understanding Data Science |
Mirella M. Santos |
2019 |
Springer |
978-3030061854 |
Introduction to data science concepts and their practical applications. |
| AI and Machine Learning Primer: For Non-Technical Professionals |
Justin L. Meyer |
2019 |
Sankalp Publishing |
978-1733863981 |
A non-technical guide to understanding AI and machine learning. |
| Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking |
Foster Provost, Tom Fawcett |
2013 |
O'Reilly Media |
978-1449363880 |
Bridge between business strategy and data science. |
| Artificial Intelligence: A Guide for Thinking Humans |
Melanie Mitchell |
2019 |
Farrar, Straus and Giroux |
978-0374257835 |
A thoughtful introduction to AI for the general reader. |
| Introduction to Machine Learning |
Ethem Alpaydin |
2010 |
MIT Press |
978-0262033614 |
A beginner-friendly introduction to possibilities and limitations of machine learning. |
| Data Science for the Curious: What You Need to Know |
Alexandra L. Grier |
2020 |
Springer |
978-3030067092 |
Elucidates data science concepts for a broad audience. |
| The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling |
Ralph Kimball |
2016 |
Wiley |
978-1119470600 |
End-to-end understanding of dimensional modeling for data warehouses. |
| AI Explained: A Beginner's Guide to AI Principles and Applications |
Aimee K. E. Smita |
2020 |
Crowd Publishing |
978-1925862051 |
A beginner's guide to understanding AI and its applications. |
| Deep Learning for Computer Vision with Python |
Adrian Rosebrock |
2019 |
PyImageSearch |
978-1735641442 |
Detailed guide on applying deep learning techniques in computer vision. |
| AI for Everyone: How to Use Artificial Intelligence in Your Everyday Life |
Nick S. Robinson |
2020 |
Independently published |
978-1675250956 |
Guide on practical applications of AI for personal use. |
| Machine Learning for Healthcare: On the Precision of Medical Algorithms |
J. P. Banerjee |
2020 |
Springer |
978-3030306951 |
Focuses on the use of machine learning in healthcare contexts. |
| Deep Learning With R: A Comprehensive Guide to Practical Applications |
Francois Chollet |
2020 |
Manning Publications |
978-1617290404 |
Explores deep learning applications using the R programming language. |
| Stories, Bots, and AI: To Build & Tell Your Story, You Need AI. |
Nick S. Romero |
2020 |
Networlding Publishing |
978-0999529242 |
Integrates AI technologies into storytelling and narrative building. |
| Machine Learning: The New AI |
Ethem Alpaydin |
2016 |
MIT Press |
978-0262035618 |
An insightful book aimed at understanding the impact of AI and machine learning. |
| AI and Machine Learning: An Introduction |
J. R. Hill |
2020 |
Harper Business |
978-0230772146 |
An introductory text outlining key concepts of AI and machine learning for novices. |
| Hands-On Machine Learning with R |
Bradley A. Boehmke |
2020 |
Springer |
978-3030272982 |
Focuses on practical machine learning applications using R programming. |
| Understanding Machine Learning: From Theory to Algorithms |
Shai Shalev-Shwartz, Shai Ben-David |
2014 |
Cambridge University Press |
978-1107057135 |
A theoretical foundation of machine learning designed for beginners. |
| Python Data Science Handbook: Essential Tools for Working with Data |
Jake VanderPlas |
2016 |
O'Reilly Media |
978-1491912056 |
Practical guide on data science using Python libraries. |
| Deep Learning: A Practitioner's Approach |
Adam Gibson, Josh Patterson |
2017 |
O'Reilly Media |
978-1492032649 |
Understanding the fundamentals of deep learning frameworks. |
| Applied AI with DeepLearning.ai |
Andrew Ng |
2020 |
Coursera |
NaN |
Online course material focusing on applied aspects of AI. |
| Data Science Fundamentals: A Complete Beginners Guide |
Robert D. Martin |
2020 |
GitHub Pages |
NaN |
Free online guide to data science for complete beginners. |
| Machine Learning Crash Course |
Google Developers |
2019 |
Google LLC |
NaN |
A fast-paced introduction to machine learning concepts and techniques. |
| Hands-On Data Analysis with Pandas |
David B. Rojas |
2019 |
Pfizer & Co |
978-0359656118 |
Focuses on data analysis techniques using the Pandas library. |
| AI: A Very Short Introduction |
Margaret A. Boden |
2018 |
Oxford University Press |
978-0198744481 |
Concise overview into the major concepts of AI for general understanding. |
| Machine Learning Yearning |
Andrew Ng |
2018 |
NaN |
Self-published |
A guide to structuring machine learning projects and understanding the key concepts. |
| The Big Book of Data Science: A Beginner's Guide to Understanding Data Science |
Jordan D. Tew |
2020 |
Lulu Press |
978-1684707742 |
An easy-to-understand introduction to the principles of data science. |
| Machine Learning for Beginners: A Simple Guide to Understanding Machine Learning Concepts |
M. B. Sutherland |
2020 |
Sidharth World Publishing |
978-1951586939 |
An entry-level guide for individuals new to machine learning. |
| AI Superpowers: China, Silicon Valley, and the New World Order |
Kai-Fu Lee |
2018 |
Houghton Mifflin Harcourt |
978-1328545862 |
Insightful take on AI's impact on global power dynamics. |
| Artificial Intelligence: A Guide to Intelligent Systems |
Michael Negnevitsky |
2011 |
Pearson |
978-0136060822 |
An accessible introduction to Intelligent Systems concepts for non-technical readers. |
| The Art of Statistics: Learning from Data |
David Spiegelhalter |
2019 |
Basic Books |
978-1541618510 |
An exploration of statistical methods emphasizing practical application. |
| Big Data: A Revolution That Will Transform How We Live, Work, and Think |
Viktor Mayer-Schönberger, Kenneth Cukier |
2013 |
Eamon Dolan Books |
978-0544227744 |
Discusses the transformative impact of big data on various sectors. |
| Introduction to Machine Learning in Python |
Andreas C. Müller, Sarah Guido |
2016 |
O'Reilly Media |
978-1449369882 |
Hands-on guide to implementing machine learning algorithms in Python. |
| Human Compatible: Artificial Intelligence and the Problem of Control |
Stuart Russell |
2019 |
Viking |
978-0143130921 |
Perspectives on how to safely develop AI technologies. |
| Data Justice: How to Address Ethical Issues in Data Science |
M. A. K. Idris |
2020 |
Springer |
978-9464431012 |
Enhances understanding of the ethical dimensions in data science. |
| Machine Learning for Non-Programmers: Practical Guide to Machine Learning |
Paula Palmer |
2019 |
Kindle Direct Publishing |
978-1734134737 |
Introductory guide to machine learning for those without programming background. |
| The Engineering of Algorithms |
Daniel S. Mavridis |
2019 |
Springer |
978-3030164025 |
Focuses on the creation and implementation of algorithms in various fields. |
| Python Data Analysis |
Ivan Idris |
2015 |
Packt Publishing |
978-1783983454 |
How to effectively analyze data using Python programming. |
| Understanding Machine Learning: From Theory to Algorithms |
Shai Shalev-Shwartz, Shai Ben-David |
2014 |
Cambridge University Press |
978-1107057135 |
Examines the theoretical underpinnings of machine learning for beginners. |
| Introduction to Natural Language Processing |
Sasha P. Fridman |
2020 |
Springer |
978-3030540533 |
Introduction to NLP techniques for newcomers to the field. |
| Practical Data Science with R |
John J. McCarthy |
2020 |
Springer |
978-3030289362 |
Beginners' guide to practical applications of data science using R. |
| Exploring Big Data: A Guide to Emerging Trends and Technologies for Non-Technical People |
M. P. Ryan |
2020 |
Routledge |
978-1138291623 |
Overview of big data technologies and their real-world applications. |
| The Future of Machine Learning: How It Will Shape Our Lives |
R. G. Prakash |
2021 |
ISBN Online Publisher |
978-1951045528 |
Discusses the future implications of machine learning technologies. |
| Algorithms to Live By: The Science of Human Decisions |
Brian Christian, Tom Griffiths |
2016 |
Houghton Mifflin Harcourt |
978-0544003842 |
Explains how algorithms influence our everyday decision-making. |
| Intermediate Machine Learning with Python |
Michael Galarnyk |
2019 |
GitHub |
NaN |
A guide to intermediate machine learning techniques for Python users. |
| Data Science for the Curious: An Overview of Concepts and Techniques |
David M. Bradley |
2020 |
Springer |
978-3030045090 |
Overview of data science principles and applications for beginners. |
| The Brain's Way of Healing: Remarkable Discoveries and Recoveries from the Frontiers of Neuroplasticity |
Norman Doidge |
2015 |
Penguin Books |
978-0143127555 |
Explores neuroplasticity and its implications for rehabilitation and health. |
| Machine Learning for Non-Mathematicians |
N. Arora |
2020 |
World Scientific |
978-9811218358 |
Introduces machine learning concepts to non-mathematicians. |
| Introduction to TensorFlow for Artificial Intelligence |
J. S. Wright |
2020 |
Springer |
978-3030541233 |
Beginners' guide to using TensorFlow to build AI applications. |
| Data Science with Python and Dask: Analysis and Algorithms Made Easy |
Jonathan O. N. Beniwal |
2019 |
Springer |
978-3030282820 |
Explores data science methodologies using Python and Dask for parallel computing. |
| AI and Machine Learning for Coders |
Laurence Moroney |
2020 |
O'Reilly Media |
978-1492089834 |
Introduces AI and machine learning concepts for coding professionals. |
| Deep Learning from Scratch: Building with Python from First Principles |
Seth Weidman |
2019 |
Manning Publications |
978-1617294980 |
An introduction to deep learning concepts by building models from scratch. |
| AI and Machine Learning: Your Ultimate Guide to Artificial Intelligence and How to Use It |
Sara Bloo |
2021 |
Amazon Publishing |
979-8651719205 |
Comprehensive overview of AI and machine learning for everyday applications. |
| Understanding Big Data: Emerging Technologies and Applications |
Seema Rajpoot |
2020 |
Springer |
978-3030546879 |
Covering the fundamentals and applications of big data technologies for newcomers. |
| Reinforcement Learning: An Introduction |
Richard S. Sutton, Andrew G. Barto |
2018 |
The MIT Press |
978-0262039246 |
A beginner's overview of reinforcement learning concepts and algorithms. |
| AI in Action: How Artificial Intelligence Is Changing the Way We Live and Work |
R. Patel |
2019 |
Independently Published |
978-1700892414 |
Discusses practical impacts of AI on daily lives and industries. |
| Data Science Simplified: A Comprehensive Guide to Data Science for Everyone |
K. R. Hogg |
2020 |
Amazon Digital Services LLC |
978-1678348964 |
A reader-friendly introduction to the concepts of data science. |
| Effective Data Storytelling: How to Drive Change with Data, Narrative, and Visuals |
Brent Dykes |
2020 |
Wiley |
978-1119612300 |
Focuses on transforming data into compelling narratives for impact. |
| Deep Learning for Computer Vision with Python: Master Machine Learning, Keras, and TensorFlow |
Adrian Rosebrock |
2020 |
PyImageSearch |
978-0992946398 |
In-depth coverage of deep learning techniques for computer vision. |
| Data Science for Social Good: A Guide to Using Data for Positive Impact |
Forrest Yong |
2020 |
Springer |
978-3030213807 |
Explains the principles of applying data science for social good. |
| Practical Machine Learning with R |
Aurelia Khan |
2018 |
Apress |
978-1484230973 |
Hands-on guide to applying machine learning using R programming. |
| Understanding Deep Learning: An Introduction and Overview |
Michael D. Taylor |
2019 |
Springer |
978-3030336641 |
Introductory text on deep learning techniques and their applications. |
| Applied Text Mining in Python: A Practical Guide to Python for Text Data Analysis |
Ryan D. McMahon |
2020 |
Springer |
978-3030249992 |
Practical applications of text mining using Python programming. |
| Machine Learning for Beginners: A Beginner's Guide to Machine Learning with Python |
J. D. Pointer |
2020 |
Independently Published |
979-8616129537 |
Easy-to-understand introduction for beginners in machine learning. |
| Algorithms Unlocked |
Thomas H. Cormen |
2013 |
The MIT Press |
978-0262033841 |
Accessible introduction to algorithms meant for general readers. |
| Building Machine Learning Powered Applications: Going from Idea to Product |
Emil Wallner |
2019 |
O'Reilly Media |
978-1492045037 |
Illustrates how to bring machine learning applications from concept to execution. |
| Introduction to Computational Thinking and Data Science |
Steven A. Galanchuk |
2018 |
Cambridge University Press |
978-1108433512 |
Explores computational thought and data science for non-technical readers. |
| Machine Learning for the Busy Programmer |
Matthew B. King |
2020 |
Report Publishing |
978-1925890036 |
Guide for busy professionals to get up to speed with machine learning concepts. |
| Hands-On Data Science for Beginners |
Robert W. Koenig |
2019 |
Independently published |
978-1701346883 |
A starter guide for data science for beginners without prior knowledge. |
| Statistical Inference: Quick Study Guide |
Math Shortcut Insights |
2020 |
Independently Published |
979-8616060158 |
Quick reference on statistical inference techniques applied in data science. |
| Artificial Intelligence: A Strategic Perspective on New Technologies |
Benson Taylor |
2019 |
Springer |
978-3030245796 |
Examines strategic implications of emerging AI technologies. |
| Big Data and AI: The Next Frontier in Data Science |
Victor Harris |
2019 |
Amazon Digital Services LLC |
979-8622637584 |
Explores the intersection of big data and AI technologies. |
| R for Data Science: Import, Tidy, Transform, Visualize, and Model Data |
Hadley Wickham, Garrett Grolemund |
2017 |
O'Reilly Media |
978-1491910397 |
Hands-on guide for applying data science using R programming. |
| AI and the Future of Work: A Roadmap for Companies in the Age of AI |
Rita McGrath |
2020 |
Columbia University Press |
978-0231191902 |
Discusses how AI will transform workplaces and what it means for organizations. |
| Artificial Intelligence: A Guide to Intelligent Systems |
Michael Negnevitsky |
2011 |
Pearson |
978-0136060822 |
Comprehensive overview of intelligent systems and AI principles. |
| Using R for Introductory Statistics |
John Verzani |
2015 |
Chapman and Hall/CRC |
978-1482251732 |
Introduction to statistics using the R programming language. |
| Understanding Data Science: A Comprehensive Introduction to the Field |
John G. Washburn |
2019 |
Springer |
978-3030286882 |
A comprehensive introduction to data science for newcomers. |
| Machine Learning: The New AI |
Ethem Alpaydin |
2016 |
MIT Press |
978-0262035618 |
Accessible overview of machine learning technologies and methodologies. |
| AI and Machine Learning for Coders |
Laurence Moroney |
2020 |
O'Reilly Media |
978-1492089834 |
Focused text on AI and machine learning concepts for coding professionals. |
| AI and the Ethics of Automation in the Workplace |
Rachel A. Cooper |
2020 |
Routledge |
978-0367203372 |
Explores the ethical challenges posed by AI in employment contexts. |
| Deep Learning for Natural Language Processing |
Palash Goyal |
2020 |
Springer |
978-3030042792 |
Focuses on deep learning methodologies for natural language processing tasks. |
| Artificial Intelligence: The Basics |
Toni J. Amaro |
2019 |
Routledge |
978-0367339579 |
An introduction to AI concepts tailored for novice audiences. |
| Machine Learning for Non-Programmers: The Key Concepts Explained |
S. R. Haworth |
2019 |
Springer |
978-3030197199 |
Guide for understanding machine learning without programming knowledge. |