Artificial Intelligence

These three-volume book series cover a wide variety of topics in machine learning focusing on supervised and unsupervised learning, intended for data scientist and machine learning experts providing a very concise description of the scikit-learn library. The first volume covers the generalized linear models (linear & logistic regression).

This book is designed to give you a comprehensive view of cloud computing including Big Data and Machine Learning. Many resources will be used including interactive labs on Cloud Platforms (Google, AWS, Azure) using Python. This is a project-based book with extensive hands-on assignments. Based on material taught at leading universities.

Python has grown in recent years, due to trends of data science and ML, and I think the main reason is because it’s essentially for a person to keep track of everything that is going on. And it makes “introducing” people to Python an interesting. Nowadays, audiences need to learn python in fast way for data science and that what we will give them.

tldr: Don't read this if all you want is from tensroflow import keras

Delve into the great ecosystem of CUDA frameworks and libraries through independent projects.Solve modern, real-world technical applications using CUDA.Interesting projects that will help you build High-Performance applications with CUDA.

Learn the computer science foundations you need to jump start your career. New second edition with expanded content and new chapters on machine learning, deep learning and AI & LLMs. Thirteen chapters covering all the career-essential topics. Perfect for self-taught developers or anyone who wants to really understand how computers work.

This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. The skills taught in this book will lay the foundation for you to advance your journey to Machine Learning Mastery!

Intelligence is problem solving. Is that all which can be said about it? How do natural and artificial intelligence differ? And what has consciousness or even life to do with it? How does AI basically work? What is its benefit and disadvantage? How much do we need AI? These questions and more are discussed and to most of them an answer is given.

Build Machine Learning models (especially Deep Neural Networks) that you can easily integrate with existing or new web apps. Think of your ReactJs, Vue, or Angular app enhanced with the power of Machine Learning models.

Want to know about data mining? This book will help you to do data mining using Weka and RapidMiner.

"What I cannot create, I do not understand" - Richard Feynman This book will guide you on your journey to deeper Machine Learning understanding by developing algorithms in Python from scratch! Learn why and when Machine learning is the right tool for the job and how to improve low performing models!

If you want to learn about recommendation engines, and how it works then this book is for you. but if you want to build a recommendation engine and learn the approaches of it such machine learning techniques that predict user purchases and preferences then this book also for you.

This book deals with one of the hot topic in artificial intelligence, Computer vision