Here are 100 books that The Master Algorithm fans have personally recommended if you like
The Master Algorithm.
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How many people have had a great idea and just needed to gain support within a large organization to move ahead? I have, over and over again, along with very accomplished teams. It’s often hard work to create something new. It requires both art and science. When people understand how it works, they elevate their craft and achieve more while lifting others up. Some of them even change the world. I’ve found great wisdom and amazing stories of courage and adventure from people who have already been there, done that, and written about their experiences. I hope these book recommendations broaden your perspective and inspire your imagination!
This is perhaps the best-known book on innovation inside large companies. When discussing technological change, people have mentioned it to me more than any other book—by a large margin! It includes examples from industries ranging from hard disks to excavators and even offers an earlier look at the prospects for electric vehicles.
It is especially interesting to see how past success can lead to complacency that hinders sustained growth and financial health. In the process, Christensen underscores how important it is for companies to continue innovating within—even when business is going well and even when acquisitions seem alluring and less difficult. This is a must-read because it establishes foundational knowledge on the importance of continued investment to sustain success.
Named one of 100 Leadership & Success Books to Read in a Lifetime by Amazon Editors A Wall Street Journal and Businessweek bestseller. Named by Fast Company as one of the most influential leadership books in its Leadership Hall of Fame. An innovation classic. From Steve Jobs to Jeff Bezos, Clay Christensen's work continues to underpin today's most innovative leaders and organizations. The bestselling classic on disruptive innovation, by renowned author Clayton M. Christensen. His work is cited by the world's best-known thought leaders, from Steve Jobs to Malcolm Gladwell. In this classic bestseller--one of the most influential business books…
I’m an economics professor, but I also have a column in Australia’s leading financial newspaper so I really appreciate authors who can tackle complex topics in an accessible manner. I’m also both extremely interested in and do academic research on topics to do with technologies like two-sided platforms, cryptocurrencies, blockchain, and artificial intelligence. All these books made me think harder about the big issues in these areas, and how to combine rigorous research with what is actually happening—often at breakneck speed—in the real-world digital economy.
This book helped me understand why advances in artificial intelligence are going to have a big impact on productivity and economic growth. I loved the analogies to old technologies like electrification of factories, and newer examples like how Team New Zealand used simulations to change racing tactics and boat design.
The book has an important, big idea at its heart. That idea is that AI helps organizations make better predications, and those better predictions allow organizations to be fundamentally redesigned to take advantage of this. This is where the AI productivity revolution comes from.
"What does AI mean for your business? Read this book to find out." -- Hal Varian, Chief Economist, Google Artificial intelligence does the seemingly impossible, magically bringing machines to life--driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.
But in Prediction Machines, three eminent economists recast the rise of AI…
I build and use emerging technological innovations in business, and I also teach others how they might too! I’m a serial entrepreneur and a Professor at the Wharton School of the University of Pennsylvania. As an entrepreneur, I co-founded and developed the core IP for Yodle Inc, a venture-backed firm that was acquired by Web.com. I’m now the founder of Jumpcut Media – a startup using data and Web3 technologies to democratize opportunities in Film and TV. In all this work, I'm often trying to assess how emerging technologies may affect business and society in the long run and how I can apply them to create new products and services.
This book by Steven Blank is a bible for anyone trying to understand how to build lean startups. The classic mistake that most entrepreneurs make is to go build a product soon after they develop a hypothesis about what customers want. By building products before customer discovery (i.e. verify customer needs and a scalable sales model), many products miss the mark and fail. The book explains how a lean start-up can figure out what customers want before proceeding to build products. This emphasis on a customer-centered approach rather than a product-centered approach can be the difference when it comes to finding product-market fit. A must-read for any founder!
The bestselling classic that launched 10,000 startups and new corporate ventures - The Four Steps to the Epiphany is one of the most influential and practical business books of all time. The Four Steps to the Epiphany launched the Lean Startup approach to new ventures. It was the first book to offer that startups are not smaller versions of large companies and that new ventures are different than existing ones. Startups search for business models while existing companies execute them.
The book offers the practical and proven four-step Customer Development process for search and offers insight into what makes some…
I have over 2 decades of finance control and general management experience spanning the manufacturing and retail sectors, in big names like LVMH. A finance controller’s job is all about efficiency and involves learning every new tool available that can help to achieve that goal. Through this work, I realized how many people are not ready for the tidal wave of disruption about to hit employees with AI and other technological changes. I was utterly shocked at not being able to find a single sensible guidebook with solutions actionable by workers.
The first comprehensive book on new tech and its impacts, following big steps made in AI progress in the early 2010s. The authors bring home the point that we are undergoing a watershed moment as tools no longer substitute merely for physical labor encroach on mental tasks – hence the book’s title.
After centuries of fleeing blue-collar jobs to take refuge in cerebral work, we are being left with nowhere to run. Not only that, but past technology would automate a given task, whereas the looming Artificial Intelligence is bound to intervene in many, many tasks currently handled by humans.
In recent years, computers have learned to diagnose diseases, drive cars, write clean prose and win game shows. Advances like these have created unprecedented economic bounty but in their wake median income has stagnated and employment levels have fallen. Erik Brynjolfsson and Andrew McAfee reveal the technological forces driving this reinvention of the economy and chart a path towards future prosperity. Businesses and individuals, they argue, must learn to race with machines. Drawing on years of research, Brynjolfsson and McAfee identify the best strategies and policies for doing so. A fundamentally optimistic book, The Second Machine Age will radically alter…
I’ve spent most of my life writing code—and too much of that life teaching new programmers how to write code like a professional. If it’s true that you only truly understand something after teaching it to someone else, then at this point I must really understand programming! Unfortunately, that understanding has not led to an endless stream of bug-free code, but it has led to some informed opinions on programming and books about programming.
Yes, it’s a textbook, albeit a particularly well-written one. You may already have it on your shelf, if you’ve taken a programming class or two.
I’m way too old to have used CLRS as a textbook, though! For me, it’s an effectively bottomless collection of neat little ideas—an easy-to-describe problem, then a series of increasingly clever ways to solve that problem. How often do I end up using one of those algorithms? Not very often! But every time I read the description of an algorithm, I get a nugget of pure joy from the “aha” moment when I first understand how it works.
Tim Harford is the author of nine books, including The Undercover Economist and The Data Detective, and the host of the Cautionary Tales podcast. He presents the BBC Radio programs More or Less, Fifty Things That Made The Modern Economy, and How To Vaccinate The World. Tim is a senior columnist for the Financial Times, a member of Nuffield College, Oxford, and the only journalist to have been made an honorary fellow of the Royal Statistical Society.
This is a clever and highly readable guide to the brave new world of algorithms: what they are, how they work, and their strengths and weaknesses. It’s packed with stories and vivid examples, but Dr Fry is a serious mathematician and when it comes to the crunch she is well able to show it with clear and rigorous analysis.
When it comes to artificial intelligence, we either hear of a paradise on earth or of our imminent extinction. It's time we stand face-to-digital-face with the true powers and limitations of the algorithms that already automate important decisions in healthcare, transportation, crime, and commerce. Hello World is indispensable preparation for the moral quandaries of a world run by code, and with the unfailingly entertaining Hannah Fry as our guide, we'll be discussing these issues long after the last page is turned.
I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.
This was my favorite book when I started my career. It talks about how information is processed, in an intelligent way, in the internet age. It acts as a tutorial to teach developers how to code our own ML programs, from online dating services, to document analyzer, and search engine. The author did an excellent job of explaining abstract ML algorithms with clear examples. His coding style in Python reads clearly, which makes the book more beginner-friendly.
Don’t get disappointed when you know this book is more than a decade old. It was a visionary book back in the day and it is still relevant today.
Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing,…
I have been a machine learning engineer applying my ML expertise in computational advertising, and search domain. I am an author of 8 machine learning books. My first book was ranked the #1 bestseller in its category on Amazon in 2017 and 2018 and was translated into many languages. I am also a ML education enthusiast and used to teach ML courses in Toronto, Canada.
This could be the first stop of your brand new machine learning journey. I personally like how the technical concept is translated into plain English – each chapter starts with a high-level overview of a ML algorithm or methodology, concise and clear, followed by lots of visual examples and real world scenarios. I can guarantee you won’t get lost halfway. The book focuses on getting you introduced to ML with minimal math. But if you want to grasp some more of math, the next book I recommend is waiting for you.
NOTICE: To buy the newest edition of this book (2021), please search "Machine Learning Absolute Beginners Third Edition" on Amazon. The product page you are currently viewing is for the 2nd Edition (2017) of this book.
Featured by Tableau as the first of "7 Books About Machine Learning for Beginners."
Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?
Well, hold on there...
Before you embark on your epic journey, there are some high-level theory and statistical principles to weave through first. But rather than spend…
I’m a mathematics professor who ended up writing the internationally bestselling novel The Death of Vishnu (along with two follow-ups) and became better known as an author. For the past decade and a half, I’ve been using my storytelling skills to make mathematics more accessible (and enjoyable!) to a broad audience. Being a novelist has helped me look at mathematics in a new light, and realize the subject is not so much about the calculations feared by so many, but rather, about ideas. We can all enjoy such ideas, and thereby learn to understand, appreciate, and even love math.
A primary reason to love math is because of its usefulness. This book shows two sides of math’s applicability, since it is so ubiquitously used in various algorithms.
On the one hand, such usage can be good, because statistical inferences can make our life easier and enrich it. On the other, when these are not properly designed or monitored, it can lead to catastrophic consequences. Mathematics is a powerful force, as powerful as wind or fire, and needs to be harnessed just as carefully.
Cathy O’Neil’s message in this book spoke deeply to me, reminding me that I need to be always vigilant about the subject I love not being deployed carelessly.
'A manual for the 21st-century citizen... accessible, refreshingly critical, relevant and urgent' - Financial Times
'Fascinating and deeply disturbing' - Yuval Noah Harari, Guardian Books of the Year
In this New York Times bestseller, Cathy O'Neil, one of the first champions of algorithmic accountability, sounds an alarm on the mathematical models that pervade modern life -- and threaten to rip apart our social fabric.
We live in the age of the algorithm. Increasingly, the decisions that affect our lives - where we go to school, whether we get a loan, how much we pay for insurance - are being made…
My passion for generative AI first ignited in 2016 when I spoke about it at a conference, and ever since then, I can’t stop! I've created an online course, a newsletter and even wrote a book to spread knowledge on this groundbreaking technology. As an instructor, I empower others to explore the boundless potential of generative AI applications. Day in day out, I assist clients in crafting their own generative AI solutions, tailoring them to their unique needs.
Bishop’s book laid the mathematical groundwork for me, making it a solid foundation for anyone venturing into Generative AI.
I love how it covers Bayesian inference, graphical models, and machine learning fundamentals in a clear, approachable way. I also think, in my personal opinion, that reading my book after this one would be a natural progression to understand where AI is heading, building on the core concepts that Bishop established.
Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation pro- gation. Similarly, new models…
