Machine Learning Engineering

We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning.

As tech products become more prevalent today, the demand for machine learning professionals continues to grow. But the responsibilities and skill sets required of ML professionals still vary drastically from company to company, making the interview process difficult to predict. In this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process. She'll take you through the highly selective recruitment process by sharing hard-won lessons she learned along the way.

No Calculator? No Problem! Mastering Mental Math , award-winning professor of mathematics and celebrated “mathemagician” Arthur T. Benjamin delivers 10 fun-filled lessons on how to do math in your head with confidence, accuracy, and speed - sometimes faster than a calculator. By the end of Professor Benjamin’s lessons, you’ll be able to add, subtract, multiply, and divide numbers faster than ever before. And with your newfound skills, you’ll soon find yourself amazing other people and, perhaps more important, yourself.

Discover valuable machine learning techniques you can understand and apply using just high school math. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert.

Ned discovers a hard truth about his past but will it also endanger the future of the universe? After reconnecting with an old acquaintance, Hank’s AI companion is on the hunt for more information and it just might bring them closer than ever to Twain. But the old Enemy AI has a few tricks up his sleeve. And while they pursue the villain, the crew also find themselves in the crosshairs. Having angered a shadowy sect, the hunters become the hunted.

This audiobook teaches you how to build an intelligent system from end to end and leverage machine learning in practice. You will understand how to apply your existing skills in software engineering, data science, machine learning, management, and program management to produce working systems.

Hugh Howey is known for crafting riveting and immersive tales of boundless imagination. Now comes Machine Learning, an impressive collection of Howey's science fiction and fantasy short fiction, including three stories set in the world of Wool, two never-before-published tales written exclusively for this volume, and 15 additional stories collected here for the first time. These stories explore everything from artificial intelligence to parallel universes to video games, and each story is accompanied by an author's note.

If you need a go-to guide with numerous examples and coded programs, Python Machine Learning will be the answer. If you want to train your own models and enhance them to achieve your demand, Python Machine Learning is the exact tool. Do you want to train the machines and model to carry out complex jobs with accurate date and predictions in a more efficient way? Do you want to have a quick start in the right direction to enter into the Python world?

This painstakingly compiled manuscript unravels the rudiments and generality of machine learning. It is total and all-encompassing with accurate and concise principles of machine learning.

In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image analysis, each new project builds on what you’ve learned in previous chapters. You’ll build a portfolio of business-relevant machine learning projects that hiring managers will be excited to see.

Author Martin Kleppmann helps you navigate the diverse data landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.

Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success.

The human brain is an extraordinary machine. Its ability to process information and adapt to circumstances by reprogramming itself is unparalleled and it remains the best source of inspiration for recent developments in artificial intelligence. In How We Learn, Stanislas Dehaene decodes the brain's biological mechanisms, delving into the neuronal, synaptic, and molecular processes taking place. He explains why youth is such a sensitive period, but assures us that our abilities continue into adulthood and that we can enhance our learning and memory at any age.

We are living through a revolution in artificial intelligence that is not slowing down. This major shift is based on simple mathematics, some of which goes back centuries: linear algebra and calculus, the stuff of eighteenth-century mathematics. Indeed by the mid-1850s, a lot of the groundwork was all done. In this enlightening book, Anil Ananthaswamy explains the fundamental maths behind AI, which suggests that the basics of natural and artificial intelligence might follow the same mathematical rules.

Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices.

Today's "machine-learning" systems, trained by data, are so effective that we've invited them to see and hear for us - and to make decisions on our behalf. But alarm bells are ringing. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole - and appear to assess black and white defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And autonomous vehicles on our streets can injure or kill.

This audiobook will help you get ready for the changes coming in the years ahead. Listen now. Packed with expert insights, the book addresses critical questions: How will AI alter employment, education, and global geopolitics? What ethical dilemmas will arise as humans interlace with AI through cyborgs and synthetic biology? Can we prepare for superintelligence and its utopian—or dystopian—outcomes?

Are you intrigued by the world of AI and data engineering? Do you find yourself captivated by the potential of these technologies to transform businesses and industries? If so, Data Engineering and AI for Beginners is the introductory guide you've been searching for. In today’s fast evolving digital landscape where data is lifeblood, those who harness its flow hold the keys to game-changing insights. This book addresses the very challenges and questions that have been on your mind: What is Data Engineering? How Does AI Fit into the Picture? Is This for Me, a Beginner? What Tools and ...

Machine Learning for Absolute Beginners Third Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required.

Either if you want to start your own AI enterprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and neural networks programming is the skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to program and use neural networks, and to do that Neural Networks for Beginners is the perfect way.