Machine Learning Engineering

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.

What does it mean to be smart? To be human? What do we really want from life and the intelligence we have, or might create? With deep and exclusive reporting, across hundreds of interviews, New York Times Silicon Valley journalist Cade Metz brings you into the rooms where these questions are being answered. Where an extraordinarily powerful new artificial intelligence has been built into our biggest companies, our social discourse, and our daily lives, with few of us even noticing.

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.

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.

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.

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.

Python Machine Learning is a guide to provide you with all the information required to master machine learning using Python. It will give you deep insight into methods of machine learning, deep learning, big data, data science, and more. Furthermore, it will also teach you concepts of Python and how combining machine learning with Python can result in practical solutions. The audiobook touches upon how these solutions can be applied in various fields including governance, technology, business, cybersecurity, and more.

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.

Machine learning is fast becoming an important technique used by multiple industries and in applications and research. But you don’t have to be part of a massive organization with an endless pot of money to get involved. Even beginners using the Python programming language can be a part of machine learning, and that is what this book is for. Today, the only limit to machine learning is your imagination.

Under the aegis of machine learning in our data-driven machine age, computers are programming themselves and learning about - and solving - an extraordinary range of problems, from the mundane to the most daunting. Today it is machine learning programs that enable Amazon and Netflix to predict what users will like, Apple to power Siri's ability to understand voices, and Google to pilot cars.

To most of us, it seems like recent developments in artificial intelligence emerged out of nowhere to pose unprecedented threats to humankind. But to Dr. Joy Buolamwini, who has been at the forefront of AI research, this moment has been a long time in the making.

Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first audiobook of its kind, The Kaggle Book assembles in one place the techniques and skills you'll need for success in competitions, data science projects, and beyond.

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.

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.

Three comprehensive manuscripts in one audiobook. Listen to this audiobook now if you want to learn more about machine learning!

Yes, you can succeed in an AI-driven world by unleashing your potential in machine learning! A working knowledge of AI and ML is no longer a “nice-to-have” but an “essential” in today's job market. But what if you feel lost amid the tech jargon? What if terms like “algorithms”, “neural networks”, or “data preprocessing” sound like a foreign language to you?

The goal of this book is simple: provide a perfect insight into data science from scratch. It clearly explains the difference between data science and big data, explaining the concept of big data very concisely and clearly. Operations on data are also very clearly explained in this audiobook. The book is also an ultimate guide to master data mining and data-analytic.

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.

Do you know how to build a machine learning algorithm in Python? Have you learned how to build a neural network in Python? If you have listened to the first three audiobooks in the series, you will know how to do both those things. If you want to learn more about the concepts related to machine learning and some subjects and concepts that are linked to machine learning, you have come to the right place.

If you want to unleash the power of data science research, machine learning tools, and learn how to optimize business processes with neural networks, then this is the audiobook for you...In this guide, you will learn the processes used by successful companies to turn raw data into powerful insights using open source tools and libraries such as Python, SciKit, and others. You'll also learn how to apply these methods to your own business data to help you make robust decisions.