Microsoft Azure Machine Learning: Explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks

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What you will learn

Learn to use Azure Machine Learning Studio to visualize and preprocess data

Build models and make predictions using data classification, regression, and clustering algorithms

Build a basic recommender system

Deploy your predictive solution as a Web service API

Integrate R and Python code in your model built with ML Studio

Explore with more than one case study

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AI Assistant (beta) to help accelerate your learning

Frequently bought together

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Building Web Services with Windows Azure (new)

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Total $ 145.97

Customer reviews

Rating distribution

(5 Ratings)

5 star 20%

4 star 60%

3 star 20%

2 star 0%

1 star 0%

Sushil Kumar Behera Jun 21, 2015

I appreciate this book as a new learner of predictive analysis and machine learning. This book is designed such a way that even someone who has no knowledge of data analysis can understand the bits and pieces. I am highly recommending to buy this book.

Amazon Verified review

Sam Sep 20, 2015

i got an ebook version of this book. I was hoping that this book would contain explanation on various algorithms that are currently being used in Azure Machine Learning. Instead this guides the user on how to make use of the various features in Azure Machine Learning. It walks the reader through each screen and explains its various features. It also explains the various formulae that are used in generating the report parameters. There is a basic explanation of what the formula does and how to interpret it. If you understand the concepts or have encountered them before (which most likely is the case) then you will enjoy the explanations. But some of the concepts that are very microsoft specific, like the Matchbox recommender system, the details are light. There are no references for the reader for him/her to read up more material regarding the concepts.The author has supplemented each chapter with a basic exercise on how to use the particular feature in machine learning studio. They are very basic. The author ends the book with two case studies from publicly available data. The first case study make use of TranStats data collection from U.S. Department of Transportation (DOT) which is the performance data of the arrival and departure performance of various aircrafts in United States. Some of the readers will remember a similar case study that ASA 2009 data visualization challenge. It was a good read to apply the concepts learnt to a actual case. The second case study is from Kaggle titled Africa Soil Property Prediction Challenge. It encourages user to try out their own models and there are no results for you to compare with. I liked reading the book, but it left me dry on what some of the concepts mean and why and where you would make use of them. If I was light on the theory of various modeling techniques, then this would be a difficult book to follow. As you would left pondering why does the author makes certain decisions about its various parameters.I would conclude that if you know how the models work and understand the underlying concepts then this is a good book to introduce you to Azure Machine Learning Studio. If you are looking to learn the models using Azure Machine Learning, then I would say buy this as your second or third book after you have gained some understanding of the models or be prepared to google or brush up the old textbooks in your bookshelf.

Amazon Verified review

Ricardo Camarena Nov 27, 2015

Es una buena guía introductoria, actualizada y completa acerca del uso de ML Studio. Referente a la estadística, hace los señalamientos necesarios a las fórmulas pero se requiere de libros de apoyo o conocimientos sólidos.

Amazon Verified review

Thierry Vallaud Dec 23, 2016

Azure Machine Learning est un logiciel de machine learning/data mining facile a apprendre avec une interface graphique très intuitive.On y accède via un compte Azure pour quelques euros de n'importe quel browser. On charge ces données dans le logiciel ou sur le cloud d'Azure en fonction des volumes. Le volume de données est quasi infini.Le logiciel permet tous les modèles classiques du ML : supervisés et non supervisés avec par exemple des randoms forest, des moteurs de règles de cooccurrence....Une fois au point le model est très facile à déployer sur le cloud mais on peut aussi tout récupérer. On peut rajouter du code Python ou R si nécessaires mais ce n'est pas obligatoire, l'interface permettant de tout faire une fois bien maitrisée. Le livre explique bien cela. A lire si vous voulez maitriser un des logiciels qui a probablement le plus d'avenir dans la data science..

Amazon Verified review

Kindle Customer Sep 22, 2015

Review of “Microsoft Azure Machine Learning” by Sumit MundChapter 1:1. Starting from the initial pages, the prose is a bit verbose e.g. a. “Traditionally, it has been an area for experts. Developing and deploying a predictive modeling solution using machine learning has never been simple and easy, even for experts.” – OK, enough with the “experts” theme. We get it. It’s hard. One sentence would is enough to get the point across. This level of verbosity is a hallmark of the book as there are examples of this overstatement and restatement throughout. b. “You should not misunderstand that predictive analytics…” should be simply “You should understand…” the double negative is confusing and unnecessary. c. While not painfully obvious, it is clear that English is not the author’s native language. There are places where an inappropriate word or phrase is used e.g. “This kind of algorithm takes all of the data and groups them into different clusters…” – the use of the word “them” is unusual, a more common usage would be “…groups it into different clusters”. This is jarring to a native English speaking reader. d. In many places the word “the” is dropped inappropriately – again creating a jarring experience for one fluent in English. e. Phrases are sometimes mal-formed as if directly translated from a different language.2. Too much time is spent explaining basic themes such as defining project scope, the relative value and completeness of the data, etc. These things are already known to those of us in the software development field and for those that are professionals in their related engineering fields that would find this tool very useful, restating the obvious borders on insulting.3. Model reuse – again, too many examples. We get the notion of “code reuse”.4. Whomever the editor was for this book needs to be reviewed for competency : a. Missed an OBVIOUS FAIL when starting with the VERY FIRST EXAMPLE in the section entitled “Creating and running an experiment – do it yourself”. Item 1 on the list contains the following text: “…and then choose the Black Experiment …”. I’m pretty sure the author meant “BLANK” not “BLACK”. Oops. b. Incorrect word used: “A decision tree is a set of questions or decisions and their possible consequences arranged in a hierarchical FISSION” <- word wanted is “FASHION”. Oops. c. In the “No Free Lunch” section, the sentence that begins “So in practice” is as follows: “So in practice ,whenever” <- the command and space are transposed. Oops. The worst part is that Word points this out in glaring fashion.5. The example for uploading a data set describes how it’s done – not really useful as it’s just a matter of following the prompts. THERE IS NO EXAMPLE DATASET CREATED THAT WILL BE UPLOADED! What use is a process with no concrete application? Show me how but don’t actually walk me through the create data portion of the process and then upload it? Useless. A better way of doing this would have been to show the “Enter Data” portion, actually provide a few lines of data, save it to the PC, THEN use the upload process to fetch back the newly created data.OK, that’s the bad. I would say that approximately 1/5th of the book is verbose fluff/filler – it could be removed without detracting from the knowledge and intent of the book.In general, the book needs to be gone over by one who is actually fluent in English to correct the oddities that appear in the text and to fix the obvious grammar errors. That the first example was missed is a real fail, in my opinion.The Good:1. The book does a good job of introducing the user to the Azure Machine Learning portal. Myself, having never needed to use the engine, I think the author does a decent job of walking the reader through the necessary steps to get up and running.2. The author does not overburden the reader with too much information nor does the author pound a point to death – over speak it a bit, yes, but not to the point of frustration on the users part.3. The data sets chosen as examples vary widely giving the reader exposure to a large number of unrelated analysis tasks that should cover the areas of expertise of most readers, everything from flower analysis (the Iris example) to flight delays.4. The examples build one upon the other and, for the most part, follow a good, logical sequence (see above for the one suggestion for a change).5. The author even goes so far as to introduce customization via the “Python” and/or “R” languages without drowning the user in minutiae nor smothering them with syntax of the selected language.The summary:1. If I were searching for a book that would walk me through the basics of how to use the Microsoft Azure Machine Learning system, this would be a good book to use as a first guide.2. 4 stars out of 5.

Amazon Verified review

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Microsoft Azure Machine Learning: Explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks

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