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Key benefits

Targets two big and prominent markets where sophisticated web apps are of need and importance.

Practical examples of building machine learning web application, which are easy to follow and replicate.

A comprehensive tutorial on Python libraries and frameworks to get you up and started.

Description

Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework. You will also learn to carry out web, document, and server mining tasks, and build recommendation engines. Later, you will explore Python’s impressive Django framework and will find out how to build a modern simple web app with machine learning features.

Who is this book for?

The book is aimed at upcoming and new data scientists who have little experience with machine learning or users who are interested in and are working on developing smart (predictive) web applications. Knowledge of Django would be beneficial. The reader is expected to have a background in Python programming and good knowledge of statistics.

What you will learn

Get familiar with the fundamental concepts and some of the jargons used in the machine learning community

Use tools and techniques to mine data from websites

Grasp the core concepts of Django framework

Get to know the most useful clustering and classification techniques and implement them in Python

Acquire all the necessary knowledge to build a web application with Django

Successfully build and deploy a movie recommendation system application using the Django framework in Python

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Paperback book shipped to your preferred address

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Amazon verified reviews

Dario Fadda Aug 20, 2016

This book explains the most important machine learning algorithms most commonly used in thecommercial real world of web development. Significantly increasing their knowledge in this field, the reader willbe perfectly able to understand, in a first time, the mathematical functions behind this technology, and then with practical methods in a real web application based on these algorithms. Its a food for thought useful to learn to use machine learning "secrets" in themost common everyday applications. A very recommended reading!

Amazon Verified review

Kunhee Lee Oct 29, 2018

Doesn't just describe the theories but shares practical advices through the author's experience.

Paolo Scattone Sep 01, 2016

Machine Learning for the Web, first publication by Andrea Isoni, PhD and Data Scientist, explores the main applications of machine learning, a programming area based on Python language.The work can be considered divided into two connected section and it is analyzed in eight chapters.The first part illustrates the key concepts of machine learning, the use of libraries for the management and analysis of data extracted from the web and provides a broad overview about the most common systems used in commercial and financial area.The second one introduces the reader to the main features of, Django, web framework for developing applications, and concludes with practical examples of the knowledges acquired.The whole discussion is made in a particularly effective educational approach: each chapter includes theoretical information and is followed by numerous application formulas explained in a comprehensive and detailed way.Machine Learning for the web is an interesting work, very well structured and recommended to all those who are interested in developing skills in a professional environment that in the near future will have a sure positive impact in companies activities in the commercial and financial sectors.

yogafreak Aug 22, 2016

I'm a newcomer to the ML world. This book was a fun read, with thorough explanations of the most common techniques. Jupyter notebooks with exercises are provided too through the author's Github page, and they help a lot getting a feeling of the performance of each method.With a standard Physics or Engineering-level mathematical background, some additional vocabulary needs to be learned to follow some of the most technical sections. In any case, the author often refers to additional manuals and resources when needed, without interrupting the train of thought.Python 2.7 is the language of choice, but all exercises I tried to reproduce worked almost flawlessly on Python 3.4 (besides some easy changes like print-> print()).Overall, the structure of the book makes perfect sense, guiding the reader through a lot of examples and real-life situations. I could take a few examples and apply them to my own (Astrophysical) research almost straight away.Bottom line, nice book that I recommend for people entering the ML world, provided that they have some mathematical background. But it shouldn't be a problem, you don't want to learn ML without knowing some math, don't you ;)?

VP Aug 18, 2016

wow...this book is simply fantastic! There is the math theory and a lot of examples with source code. If someone must code a predictive python web application, this is the right book to start and to understand how to make it. There is a lot of examples for python libraries and obviously for Django. I think these examples, In some cases, are general purpose and not only for the book purpose. All documented and all explained also for beginners. Highly recommended!

Machine Learning for the Web: Gaining insight and intelligence from the internet with Python

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