This book is specifically designed for readers who already have a relatively good understanding of programming. It serves as a comprehensive and fast-paced introduction into the essentials of Tensorflow & Scikit-Learn, and how to use them to build intelligent models and systems. Keep in mind that familiarity with Python is a necessary pre-requisite before reading. This book is jam-packed with exercises to consolidate understanding, and covers a broad range of topics with varying complexities across the Machine Learning process.
If you are extremely interested in the concept of Machine Learning, but lacking the technical knowhow to make sense of it all, then this is the book for you. Designed for people that have no background in coding or programming, it pretty much does what it says on the tin. The book provides simple and visually engaging examples, and interactive exercises to assist you in understanding concepts that may have been previously out of reach. This is an excellent book for beginners who want to understand the terms and get introduced to the subject.
This book provides a detailed collection of Machine Learning algorithms. It serves as a good introduction for beginners who have a stronger grasp of mathematics and are looking to understand Machine Learning more from this angle. A great book for people that not only want to learn some of the basic underlying principles in Machine Learning, but that also want to see how this progresses into practical application.
This book will help you draw insights from complex datasets and apply the correct algorithms for solving specific problems.You will learn to apply Machine Learning methods to deal with tasks such as forecasting, image classification, prediction and clustering. Machine Learning with R will help you to gain a brief understanding of a wide scope of subject matters, but might be less suited for people that want to get deeper insights in a specific field.
This book is not really focused on the technical side, and is instead more for people that are interested in some of the philosophies that surround the incorporation of Artificial Intelligence in future society. More specifically, it looks at the relationship between the human mind and AI. More specifically, how this relationship could likely evolve over time. It should be noted that this isn’t the only view on this subject but Kurzweil makes some fascinating points that should not be readily ignored.
As the title might indicate this book is much more focused on the business processes that can support future Artificial Intelligence initiatives. This book is much less about the technological side, and far more about how to ripen your organisation for Artificial Intelligence and develop an internal culture and climate that is conducive for this. This book will not overload you with technical jargon, but will give you simple explanations and practical advice for setting up Machine Learning & AI projects in your company.
Deep Learning with Python was written by the creator of Keras. It’s principle aim is to build your understanding of Deep Learning, and it uses a number of examples and exercises to walk you through this process. Some of the topics included are computer vision, natural-language processing, and generative models. There are also plenty of challenges scattered around the book to strengthen your knowledge in these areas. This book might be more geared towards people with pre-existing knowledge in these areas, and are instead looking to further clarify some tricky subject areas.
This book might strike one of the best balances between technical robustness and clarity of explanations. It isn’t going to drown you in drawn-out algorithms or mathematical equations when explaining core concepts in statistical learning, but it will give you enough to ensure a well rounded understanding of the subject. This book can serve as an extremely useful reference, acting as a compendium for need-to-know concepts in the fascinating world of statistical learning.
This book is best read with a good understanding of linear algebra, probability, calculus, and preferably some statistics. That being said, no understanding of pattern recognition or Machine Learning itself is required, which makes it a good introduction for people with existing statistical backgrounds. It will provide a very in-depth understanding of key topics, specifically in Machine Learning and Bayesian methods.
This book is a good introduction on most things to do with Machine Learning (not really Neural Networks though). It can help you with understanding a lot of actual applications through a probabilistic lens. Many consider this to be one of the best books out there on Machine Learning, with a good mixture of introduction, relevancy and attention to detail.
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