This dataset contains information about various books related to artificial intelligence and machine learning. Each entry provides details such as the title, author, publication year, publisher, ISBN number, and a brief description of the book's content.
| Title | Author | Year | Publisher | ISBN | Description |
|---|---|---|---|---|---|
| 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. |