If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
There are lot of Machine Learning tutorials present online however, we always face one issue (the one I faced:P) that is the order of tutorial. And I believe that "Learning without a vision is a waste". Because there are so many things to learn in this world and if we gather info & learn anything without a goal, we will be going to waste our time and eventually forget what we learnt. Everyone has different opinion but this is what I believe. Hence, I have curated a list of free Machine Learning Ebooks from different sources.
Introduction to Algorithms, the'bible' of the field, is a comprehensive textbook covering the full spectrum of modern algorithms: from the fastest algorithms and data structures to polynomial-time algorithms for seemingly intractable problems, from classical algorithms in graph theory to special algorithms for string matching, computational geometry, and number theory. The revised third edition notably adds a chapter on van Emde Boas trees, one of the most useful data structures, and on multithreaded algorithms, a topic of increasing importance.
I recently was a part of an interesting Reddit discussion and a few of my answers got highly upvoted. The main point of it was the untold truths of being a machine learning engineer. I am sharing the key takeaways in a curated manner as I was one of the more active participants. Many Machine Learning enthusiasts think that they will play with fancy Deep Learning models, tune Neural Network architectures and hyperparameters. Don't get me wrong, some do, but not many.
Data science is an interdisciplinary field that contains methods and techniques from fields like statistics, machine learning, Bayesian, etc. They all aim to generate specific insights from the data. Today let's list do something like Huge List of Free Artificial Intelligence, Machine Learning, Data Science & Python E-Books. So, today we're gonna to list down down some excellent data science books which cover the wide variety of topics under Data Science. Starting with... 1. Python Data Science Handbook Python Data Science Handbook explains the application of various Data Science concepts in Python.
With further time spent at home looming, we have gathered 20 resources which are free to access for your continued learning. The below list includes free e-courses & e-books. Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises with over fifteen hours of accessible education. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. This open source video lecture series includes 23 full-length seminars, starting with an introduction and scope, later going on to cover topics including rule-based expert systems, neural networks, felicity conditions and more.
From food delivery and disinfecting offices to retail services and surgeries, robots are increasingly sharing our workplaces. How are enterprises adapting to the explosive growth in robotics and robotic systems? ZDNet and TechRepublic published a PDF ebook: Robotics in the enterprise to find out. In "Robotics in business: Everything humans need to know," ZDNet contributor Greg Nichols provides an executive guide to the technology and market drivers behind the $135 billion robotics market. ZDNet's Daphne Leprince-Ringuet investigates what work will look like as robots start mingling with humans in their workplaces in her feature, "The robots are coming, and this is how they will change the future of work."
The book is about quickly entering the world of creating machine learning models in R. The theory is kept to minimum and there are examples for each of the major algorithms for classification, clustering, features engineering and association rules. The book is a compilation of the leaflets the authors give to their students during the practice labs, in the courses of Pattern Recognition and Data Mining, in the Electrical and Computer Engineering Department of the Aristotle University of Thessaloniki.
Deep learning is getting a lot of attention these days, and for good reason. It's achieving unprecedented levels of accuracy--to the point where deep learning algorithms can outperform humans at classifying images and can beat the world's best GO player. If you are interested in using deep learning technology for your project, but you've never used it before, where do you begin? Should you spend time using deep learning models or can you use machine learning techniques to achieve the same results? Is it better to build a new neural network or use an existing pretrained network for image classification?