Amazon calculates a product’s star ratings based on a machine learned model instead of a raw data average. The model takes into account factors including the age of a rating, whether the ratings are from verified purchasers, and factors that establish reviewer trustworthiness.
I am not a machine learning expert. I am not a machine learning novice. But as I am coming to understand a big part of machine learning is really all about curve fitting - and, as an engineer involved with scientific computing, I do know something about that. I have been interested in machine learning for the past year or so and wanting to educate myself to see if it can use it in my job I have been searching for just the right book. Many of the books that are out there seem to be too simply e.g. not enough math. Others are too mathematical and don't explain with words well enough. Others intimately combine ML with programming (usually in Python) to the point whether you learn neither ML or programming. This book "Machine Learning Refined" is different. It seems to be the first book I have found that is rigorous from the beginning (Chapter 2) after a clear introduction and motivation in Chapter 1. The mathematics are not very high level nor are they simplified too much. They are presented at just the right level to be useful. I have breezed through the first few chapters (I usually give up after Chapter 1 or beginning of Chapter 2 in these "new" subjects) and found them crystal clear with great illustrations (even in the Kindle version). Another impressive feature that I have not had time to dive into yet is the extensive end of the chapter exercises. These look like they were assembled with tender loving care to be both interesting and useful to demonstrate the chapter materials. I am also anxious to download and try out the code and other resources available at [...]. The price, even for the Kindle edition, is a bit high but the quality you get over the more popular "tech" titles is clearly worth it. If you have a basic math background and an interest in truly learning ML this is the book for you. I highly recommend it.
I love this book. As the name suggests, all the essential ML concepts are refined in a sub 300 page book that is self contained. The book's clarity without sacrificing mathematical rigor with tons of end of chapter exercises that reinforce concepts make it a unique book. The support site has exercises in python and matlab and additional ipython notebooks for better understanding. The authors are incredibly responsive and supportive. The clarity and exposition condensed in this book is reminiscent of prof Yaser's Learning from Data, but much more practical. I purchased this book after following the author's free chapters online. Totally worth it.
The book is concise and has all of the content needed to get the foundations down. But that being said, it requires a moderate to advance college mathematics understanding to move forward in the book. So if you are a mathematics major or have taken all of the core mathematics courses then this book is for you.
I've take a couple of classes with Jeremy Watt and he has a tremendous skill of f explaining complex machine learning concepts in a way that anyone could understand. The book is a wonderful capture of that as well . What I enjoy about it is its fluid description of the complex, theoretical side, explained in such a way that you can confidently go out and apply Machine Learning skills in the real world. I would highly recommend this for anyone interested in learning the concepts and excited to make a difference in the world with Machine Learning.
If you are an instructor looking for a textbook this would probably be a good choice. But extraordinarily important things needed for implementation (like the matrix notation for the computation of certain gradients) are left as exercises with no solution key. So as a handy reference it has questionable utility - which is a shame given its clarity and compact length.
This book is one of the best introduction into Machine Learning. Very well organized and well written. It doesn't go into very advanced topics, but it is a great resource for understanding the basic concepts and also for your interviews in ML
This textbook is actually excellent - I took three classes that used content from it and the book was fantastic at building up concepts from simpler ones that we understand. The strategy used in every chapter is developing real, interesting examples by starting from familiar ones or toy problems, and this textbook excels at it. The book also provides plenty of Python examples for you to solidify your understanding of the concepts, ranging from "implement the details of this algorithm" to "use this algorithm to solve a high level problem." This variety in the levels of abstraction makes it an especially effective teaching tool.
Jeremy also manages to strike the right balance of explaining concepts mathematically without getting too far into the weeds of proofs. Too many introductions to machine learning feel like they gloss over the mathematical details, and that is never the case in this book. The first class that I took using this book was super helpful to developing my mathematical maturity and showing me applications of things that I'd learned in linear algebra or vector calculus.
The book is well-organized, includes plenty of examples, and helps you to develop a deep understanding of machine learning concepts and algorithms. The knowledge I gained from this book helped me to find a data science job, and I still maintain that this book is better than nearly all machine learning resources out there.
Jeremy and Reza did a great job on demonstrating complicated machine learning algorithm and application in a very vivid way. In one way, this book fits persons who would like to learn machine learning concepts from scratch. The figurative in the book is so well-designed that you can understand it easily if you take the math formula as nightmare like me. In the other, if you have some foundation in machine learning, the fruitful examples can give you a sense on how these algorithms are applied into different applications. Moreover, the book itself is an arts. You can't imagine how excited am i when i received this book. With the time being, I liked it even more. I kept feeling myself is discussing the ML concepts with authors when i was reading it. Recently, i was preparing an interview to get in ML industry. I am using this book as a good reference. I explain every concepts clearly to the interviewer and hit a job offer. PLEASE DON'T WAIT IF U ARE SEEKING A FANTASTIC MACHINE LEARNING BOOK.
This book is an excellent introduction to machine learning. It is very clear and well written. The book focuses on the foundational aspects of machine learning and develops them from the ground up in a mathematically rigorous, yet accessible and highly inspiring way. This more theoretical presentation is complemented by many example applications, which illustrate how machine learning algorithms can be used to solve real-word problems. The authors do an amazing job explaining the presented algorithms and motivating each individual step in mathematical derivations. In addition to the book, the authors provide example code in Python and Python notebooks with interactive illustrations of the material on the book website. I highly recommend this book to anyone trying to truly understand machine learning.
The book has a very clear explanation and a novel treatment of some ML concepts. For example, it uses LogSumExp trick (instead of MLE) to introduce logistic regression right from perception algorithm and even go deeper to other classification methods such as SVM, etc. The treatment is very systematic and shows the connection between different methods. And it explains transformed feature space using very approachable but rigorous level of math and even goes to a pretty high level. I would recommend this book to both practitioner in the field and anyone interested in the theory.