| Title | AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence |
| Author(s) | Laurence Moroney |
| Publisher | O'Reilly Media |
| Year | 2020 |
| Edition | 1st Edition |
| Language | English |
| Pages | 390 |
| ISBN | 9781492078197 |
| Genre / Domain | Computers, Artificial Intelligence, Machine Learning |
| Series | Unknown |
| Size | 34.55 MB |
| Extension |
Summary
AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence is a practical entry point into the world of machine learning, written specifically for working programmers who want to transition into AI development without being overwhelmed by advanced mathematics. Authored by Laurence Moroney, who leads AI Advocacy at Google and has taught AI to over one million students through Coursera and Harvard, this O'Reilly Media publication draws directly from his extremely successful AI courses [citation:3][citation:10]. The book's core premise is that you do not need a PhD in mathematics to build meaningful AI applications; you need a willingness to engage with code and a structured path that moves from simple concepts to deployable models. Published in 2020 and running to 390 pages, this guide has become a standard reference for programmers seeking to add machine learning skills to their professional toolkit.
The book's approach is decidedly code-first, meaning that theory is introduced only as needed to support practical implementation. Early chapters establish the fundamentals of machine learning using TensorFlow, Google's open-source framework for building and training neural networks [citation:1]. Readers learn how to construct models through hands-on code samples, understanding concepts like layers, activation functions, and loss functions by writing and executing them rather than through abstract mathematical derivation. This pedagogical choice reflects Moroney's conviction that the barrier to AI entry is often the perceived mathematical complexity, not the actual difficulty of the concepts themselves. By the end of the introductory sections, readers have built and trained their first models and understand the basic workflow of data preparation, model definition, training, and evaluation.
The middle and later chapters expand into the most common and commercially valuable machine learning scenarios. Computer vision is covered extensively, including feature detection and image classification, with readers learning how to build models that can recognize patterns in visual data [citation:1]. Natural language processing follows, teaching techniques for tokenizing and sequencing words and sentences, enabling models to work with text data. Sequence modeling prepares readers for time-series and language-based applications. What distinguishes this coverage is its practical orientation: rather than surveying the entire field of AI, the book focuses on the specific applications that employers most commonly need. The final sections address deployment, showing how to embed models in Android and iOS applications for on-device inference, and how to serve models over the web and in the cloud using TensorFlow Serving [citation:4]. This end-to-end perspective—from training to deployment—gives readers a complete picture of the AI development lifecycle.
The target audience is explicitly programmers, not data scientists or mathematics students. The ideal reader has some programming experience, likely in Python, and wants to understand how to build AI systems without pursuing a formal academic specialization. Career-changers from web or mobile development, software engineers seeking to expand their skillset, and self-taught developers who have found other machine learning books too theoretical will all find the material accessible. The book assumes no prior machine learning knowledge and deliberately avoids the daunting mathematical formalism that characterizes many AI textbooks. However, it does assume comfort with code and a willingness to experiment, since every concept is introduced through executable examples rather than prose alone [citation:1].
What makes this book valuable in a crowded field is its author's unique position at the intersection of industry practice and mass education. Moroney's work at Google has given him direct insight into what skills employers actually seek, and his experience teaching over a million students has refined his ability to explain complex topics accessibly [citation:3][citation:10]. The book has been widely adopted by libraries and universities, appearing in the catalogs of Stanford, Yale, and the University of Wisconsin, among others [citation:1][citation:2][citation:9]. For readers who have tried other machine learning resources and found them either too abstract or too superficial, this guide offers a middle path: rigorous enough to build real competence, practical enough to maintain momentum, and structured enough to serve as a complete curriculum for self-study.
Key Features
- The book employs a code-first approach, teaching machine learning concepts through executable TensorFlow examples rather than requiring advanced mathematical prerequisites.
- It covers the most common and commercially valuable machine learning scenarios, including computer vision, natural language processing, and sequence modeling.
- Deployment is treated as a first-class concern, with dedicated sections on embedding models in Android and iOS applications for on-device inference.
- TensorFlow Serving is introduced to demonstrate how trained models can be deployed over the web and in the cloud for production use.
- The content is derived directly from Laurence Moroney's widely successful AI courses, which have reached over one million students on platforms including Coursera and Harvard [citation:10].
- Computer vision coverage includes practical techniques for feature detection in images, enabling readers to build models that recognize visual patterns.
- Natural language processing instruction focuses on tokenizing and sequencing words and sentences, foundational skills for any text-based AI application.
- The book is designed for programmers, meaning examples are written in code that readers can run, modify, and learn from directly.
- It avoids the daunting mathematics that deters many programmers from pursuing machine learning, introducing theory only as needed to support implementation.
- The author's role as AI Advocacy Lead at Google provides insight into industry best practices and employer expectations [citation:3].
- Each concept builds on previous material, creating a structured progression from basic model building to deployment in multiple runtime environments.
- The book serves as an accessible entry point for career-changers moving from traditional software development into AI specialist roles.
About the Author
Laurence Moroney is a leading voice in artificial intelligence education and advocacy, currently serving as the AI Advocacy Lead at Google, a position he has held since 2013 [citation:3][citation:14]. In this role, he works with Google AI Research and product development teams to expand access to AI and machine learning for developers worldwide. He is a best-selling author who has written dozens of programming books, including AI and Machine Learning for Coders and AI and Machine Learning for On-Device Developers, as well as several acclaimed science fiction novels [citation:3]. Moroney is also a prolific educator, having taught over one million students through massive open online courses on Coursera and Harvard, and he is a frequent contributor to the TensorFlow YouTube channel and a recognized global keynote speaker [citation:10][citation:8].
Moroney's path to AI was unconventional. He holds a bachelor's degree in physics and computer science from Cardiff University in Wales [citation:3]. After graduating into an economic recession in the late 1980s, he was selected for a British government program to train a cohort of AI specialists—an initiative that ultimately failed due to the limitations of available tooling at the time [citation:6][citation:7]. He later received a fully funded scholarship to pursue a Master's degree and went on to work at Microsoft as a Senior Developer Evangelist and Product Manager before joining Google [citation:3][citation:14]. His career has also included roles at Harmon.ie and Reuters, and he currently serves as a Fellow at AI Fund, advising founders on the feasibility of their machine learning applications [citation:3]. Beyond technical writing, Moroney is an active member of the Science Fiction Writers of America and has produced screenplays for television, including an episode of Stargate Universe [citation:3].
Related Books
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow — Aurélien Géron
- Deep Learning with Python — François Chollet
- AI and Machine Learning for On-Device Developers — Laurence Moroney
- Programming Machine Learning — Paolo Perrotta
- TensorFlow for Deep Learning — Bharath Ramsundar and Reza Bosagh Zadeh
- Machine Learning for Absolute Beginners — Oliver Theobald
- Grokking Deep Learning — Andrew W. Trask
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FAQ
Q : Do I need advanced mathematics to understand this book?
R : No, the book is explicitly designed for programmers without advanced mathematical backgrounds. Moroney's code-first approach introduces theory only as needed to support implementation, avoiding the daunting calculus and linear algebra that characterize many machine learning texts. The author has noted that his own experience with mathematics-heavy training felt disconnected from practical application, which motivated this pedagogical choice [citation:7]. Readers need only basic programming proficiency and a willingness to engage with code examples.
Q : Which framework does the book use for building models?
R : The book uses TensorFlow throughout, Google's open-source framework for machine learning. This choice reflects Moroney's role at Google and his involvement with the TensorFlow ecosystem. Readers learn to build models with TensorFlow using skills that employers actively seek, as the framework is widely adopted in industry. The book also covers TensorFlow Serving for deploying models in production environments.
Q : Can this book help me transition from a programming job to an AI specialist role?
R : Yes, that is the book's explicit purpose. As the subtitle indicates, it is "a programmer's guide to artificial intelligence," designed for those looking to make a career move from general programming into AI specialization. The content focuses on skills employers desire, and the author's position at Google provides insight into industry expectations. By covering the full lifecycle from model building to deployment on mobile, web, and cloud, the book prepares readers for practical AI development work.
Q : What types of machine learning applications does the book cover?
R : The book covers the most common and commercially valuable machine learning scenarios: computer vision, natural language processing (NLP), and sequence modeling. Specific topics include feature detection in images, tokenizing and sequencing words and sentences for text applications, and preparing models for deployment across different runtime environments. This focused coverage ensures readers develop practical competence in areas most likely to be needed in professional settings.
Q : Does the book teach how to deploy models in mobile applications?
R : Yes, deployment is a significant component of the book. Readers learn methods for embedding models in both Android and iOS applications, enabling on-device inference. This is increasingly important as mobile AI applications become more prevalent and as privacy concerns drive computation to edge devices. The book also covers serving models over the web and in the cloud using TensorFlow Serving, providing a complete picture of deployment options.
Q : Is this book based on the author's online courses?
R : Yes, the book draws directly from Laurence Moroney's extremely successful AI courses, which have reached over one million students through platforms including Coursera and Harvard [citation:10]. This origin means the material has been tested on a large audience and refined based on what works pedagogically. Readers who have found his courses helpful will find the book a natural extension, while those new to his teaching will encounter a proven approach to explaining machine learning concepts.
Q : What is the difference between this book and more theoretical machine learning texts?
R : The fundamental difference is the code-first approach. While theoretical texts begin with mathematical foundations and build toward implementation, this book begins with executable code and introduces theory only as needed to understand what the code is doing. This makes the material immediately actionable—readers are building and training models from the early chapters. The trade-off is that the book may not provide the deep mathematical understanding that some specialized roles require, but for practical AI development, it offers a faster and more engaging path to competence.
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