Free Ebook Real-World Machine Learning
Whatever your problem, analysis will always give you simple scenario to be much enjoyable. Yeah, the enjoyment publication will certainly reveal you its power making individuals rejoice and also laugh. The social book will certainly offer you brand-new knowledge everyday about this life and also social relationship. Politics and spiritual, something is huge now. It is also concerning how the people will certainly honour publication, every sort of publication as the referred analysis product. We could start it from the Real-World Machine Learning
Real-World Machine Learning
Free Ebook Real-World Machine Learning
Testing the mind to think better and also quicker can be undergone by some means. Experiencing, listening to the various other experience, adventuring, studying, training, and extra sensible activities may assist you to enhance. Yet here, if you don't have adequate time to get things straight, you could take an extremely easy means. Reading is the simplest activity that can be done almost everywhere you desire.
Also the cost of a book Real-World Machine Learning is so affordable; numerous individuals are really stingy to establish aside their cash to acquire the books. The other factors are that they feel bad and have no time to head to guide establishment to look guide Real-World Machine Learning to read. Well, this is contemporary era; many books could be obtained quickly. As this Real-World Machine Learning and a lot more books, they could be entered very quick methods. You will certainly not have to go outside to get this book Real-World Machine Learning
Providing the ideal book for the right procedure or problem can be a selection for you that really want to take or make take care of the opportunity. Reading Real-World Machine Learning is a manner in which will certainly overview of be a far better person. Even you have actually not yet been a good person; at the very least learning to be better is a must. In this instance, the issue is out your own. You need something brand-new to encourage your willingness actually.
This publication is offered in soft data kinds. You could download it. One that will affect you to read this publication is that it can be your very own selection to make much better really feels. Your life is yours. And choosing this Real-World Machine Learning as your reading product is also your option. However below, we truly advise you to read this publication. You can locate exactly what the aspects we present. Just get this book and read it, so you can obtain the reasons of why you need to check out.
Product details
Paperback: 264 pages
Publisher: Manning Publications; 1 edition (September 30, 2016)
Language: English
ISBN-10: 9781617291920
ISBN-13: 978-1617291920
ASIN: 1617291927
Product Dimensions:
7.3 x 0.7 x 9.1 inches
Shipping Weight: 1 pounds (View shipping rates and policies)
Average Customer Review:
3.9 out of 5 stars
9 customer reviews
Amazon Best Sellers Rank:
#733,208 in Books (See Top 100 in Books)
This is a great book on the subject, focusing on real-world applications of machine learning.It is not an introductory book on this subject.Readers interested in the mathematical foundations of machine learning are advised to refer to textbooks such as "An Introduction to Statistical Learning", by Gareth James.The authors demonstrate their hands-on knowledge of the subject by presenting the material in a cohesive fashion with several examples anduse cases accompanied by Python, pandas and scikit-learn Notebooks. Two of the authors were amongst the co-founders of Wise.io, which was acquired by General Electric, a testament to the business value of their body of knowledge beyond this book.In terms of content, the strengths of the book are in its coverage of feature engineering and scaling of machine learning systems.In addition, the example Notebooks and associated data are available for download.Its weakness is in presentation of figures in black-and-white, which makes them less than useful. Yet, Manning makes access to the PDF version of the book, containing figures in color, relatively easy.
While this is a practitioner oriented book, it will be useful to anyone who is learning about machine learning. This book does a very good job of illustrating the "how" of machine learning--- the practical steps of organizing data sets, and the various steps involved in building up and evaluating models.The book is very well organized, and well presented. The authors crafted a good systematic approach for explaining and illustrating through example the various steps of the process of using ML methods to create models for classification and prediction. They book has a number of good examples.No mathematical background is required for reading this book. Obviously it helps if the reader has some familiarity with the various types of statistical models used in ML. Even if that is not the case, the book is a good starting point for bridging between the "what" of ML and the "how" of ML. For those who want to try things in a hands-on fashion, they give a number of code examples, with sufficient brief annotations so you know what the blocks are code are being used for.
It provides a decent overview of ML, and has some great project ideas, but is way too skimpy on some if the important mathematical details. Also, a lot of the functions they demonstrate code fof, especially for feature engineering, already exist in many ML packages.
This is a great work for the intermediate-level developer - some basic familiarity with Python coding is assumed, along with basic understanding of algorithms - so it makes a really good companion for programmers wanting to adopt machine learning practices and problem-solving. It really emphasizes best practices in terms of data preprocessing and taking an approach to using ML the right way - not just as a catch-all tool.It's a very helpful guide that'll make a great addition to your library!
"Real-World Machine Learning" was the antidote after going through a couple of ugly, half-baked and semi-competent "book products" from Packt. It is uplifting to see an original, expert, well-written and visually attractive book.Trying to describe it, I would note three things that the book is not. It is not obviously more "real world" than its competitors: the "real world" reference seems to be a forgivable differentiation exercise. It is not thick: 230 pages. It is not a textbook or a catalogue of machine-learning algorithms - which you will need to get. (I would suggest "Introduction to statistical learning" by James, Witten, Hastie and Tibshirani). It is, however, a thoughtful introduction to and overview of machine-learning methods, appropriately remembering about the context and life-cycle of an ML project, and keeping things hands-on with small Python examples, but managing not to fall into the catalogue mode.I have seen other books try this before. "Doing Data Science" by O'Neill and Schutt comes to mind first, long on enthusiasm but a little short on quality. Then there is Manning's own "Practical Data Science with R" by Zumel and Mount. Among the three, RWML looks like a clear winner.If I had to pick on something, I would register disappointment with the book's one extended exercise, based on the NYC taxi dataset. After all the thoughtful discussion, an unimaginative take-all-variables-and-dump-them-into-an-algorithm-then-look-at-single-number exercise was a let-down. (Statisticians, taught to think hard about model specification and to prize model interpretability, often have that complaint about machine-learning hotshots. Google Norman Matloff's blog post "Statistics: Losing Ground…" for more on the differences between the two camps). This said, from editorial viewpoint, maybe not getting into the weeds actually was a good idea.An enthusiastic endorsement for a very nicely done book.
I liked the textual part of the book. It's quite a good introduction for someone who haven't dealt with ML before. I got the overall image of what it is, the common workflow, and that you can start experimenting relatively easy.In contrary, code snippets in the book and python notebooks seemed like an afterthought — unrelated lines of code, sometimes lacking explanation while having it for the obvious parts. I gave up trying to run the notebook for the chapter 6, it had missing imports, passing NaNs to functions that don't expect them, and other errors which only Google knows how to fix.The discussion forum for the book is also dead, and the search doesn't work, so you won't find support there as well.
Real-World Machine Learning PDF
Real-World Machine Learning EPub
Real-World Machine Learning Doc
Real-World Machine Learning iBooks
Real-World Machine Learning rtf
Real-World Machine Learning Mobipocket
Real-World Machine Learning Kindle