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Machine Learning for the Web, by Andrea Isoni
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Key Features
- 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.
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.
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
Andrea Isoni is a data scientist, PhD, and physicist professional with extensive experience in software developer positions. He has an extensive knowledge of machine learning algorithms and techniques. He also has experience with multiple languages, such as Python, C/C++, Java, JavaScript, C#, SQL, HTML, and Hadoop.
Table of Contents- Sales Rank: #224810 in Books
- Published on: 2016-07-29
- Released on: 2016-07-29
- Original language: English
- Dimensions: 9.25" h x .68" w x 7.50" l, 1.14 pounds
- Binding: Paperback
- 298 pages
About the Author
Andrea Isoni
Andrea Isoni is a data scientist, PhD, and physicist professional with extensive experience in software developer positions. He has an extensive knowledge of machine learning algorithms and techniques. He also has experience with multiple languages, such as Python, C/C++, Java, JavaScript, C#, SQL, HTML, and Hadoop.
Most helpful customer reviews
4 of 5 people found the following review helpful.
Machine learning for novice data scientists and technicians, with lots of excellent coding examples in Pyhton
By Stefano Sabatini
Machine Learning for the Web by Andrea Isoni can be considered as an introduction to machine learning tools in Python for technicians already acquainted with machine learning theory.
The first chapters give a short overview of the most important machine learning algorithms used in machine learning scientific and industrial environments, and gives a short introduction to the adopted Python libraries to process data employing those algorithms.
The second part is more focused on technological aspects, and shows how to use Python to implement a web application complete with a recommendation and sentiment analysis system using the django framework and various Python libraries.
A somehow more detailed summary follows.
Chapter 1, Introduction to Practical Machine Learning Using Python
The more important Python libraries employed in the book are introduced here. This chapter can be considered as a tutorial on the various libraries, namely: NumPy, pandas, matplotlib. For the first two a complete tutorial is given, for the last one (matplotlib) only a short introduction. Other libraries are used in the book (namely: SciPy, scikit-learn (sklearn), scrapy), and are introduced later in the book.
Note that this chapter assumes the reader to have a basic understanding of the Python language (although not advanced programming topics are required to fully enjoy the book).
Chapter 2, Machine Learning Techniques: Unsupervised Learning
This chapter recalls the most important unsupervised learning algorithms, in particular clustering and data reduction algorithms. Every description
is complemented by a coding example in Python using numpy and the other libraries.
Note that although all the algorithms are fully described, the chapter assumes a detailed knowledge of the mathematical notation and understanding of the algorithms. So this is more to be intended as a reminder for the student rather than a full mathematical introduction to the main concepts.
Chapter 3, Supervised Machine Learning
This chapter provides an overview of the most employed supervised machine learning models: it starts with an explanation of the model error estimation, and proceeds with a description of several methods: based on linear regressions (ridge, lasso, logistic), k-nearest neighbours (KNN), Naive Bayes and Multinomial Naive Bayes, Gaussian Naive Bayes, decision trees, SVM.
The models are then compared using a testing dataset with numpy/pandas/sklearn and measuring several performance indices. This is done for classifiers and regression models using two different public datasets.
The chapter ends with a description of the Hidden Markov Chain model, together with a Python implementation.
Chapter 4, Web Mining Techniques
This chapter is about retrieving pages from the web, storing and processing them to extract relevant information. The chapter can be divided in the following sections.
Web structure mining: explores web crawling, indexing, and page ranking (in particular, the famous Page algorithm is explained).
Web content mining: parsing is discussed using the scrapy tool, natural language processing through the nltk library. Several information retrieval models are then considered and explained from a mathematical point of view. For each considered model (TF-IDF, Latent Semantic Analysis, Doc2Vec, Word2vec), a Python example is later presented in the context of creating a movie review query system, and the models performances are compared.
Postprocessing information: postprocessing is analyzed in the context of providing two distinct goals, select topics and provide subjective information related to user estimation (opinion/sentiment analysis). Latent Dirichlet Analysis is covered to provide means of classifying a text extracting latent topics. Sentiment analysis is then performed on a corpus of movie reviews, essentially applying several classifiers (Naive
Bayes, SVM and logistic regression) and testing their performances with nltk.
Chapter 5, Recommendation Systems
Given a set of users, and a set of items, and some user evaluations on the items, a recommendation systems tries to predict the non-evaluated items which may be appreciated by the user. This chapter explores the most important techniques adopted in the industry, and as usual provides an exampling implementation in Python using scipy with data from the MovieLens database.
In particular the chapter explores Content-based Filtering (CBF) and Collaborative Filtering (CF) methods. As usual, several complete examples are provided. Also, the chapter mentions hybrid approaches to recommendations, and recommendation system performance evaluation using supervised machine learning techniques.
Chapter 6, Getting Started with Django
This chapter provides a short tutorial on setting up a django server and writing a complete application for it. The chapter explains the code for a simple application storing addresses.The chapter covers the most important element of a django application, thus at the end of the reading you should be able to get a firm grasp of how it works.
Chapter 7, Movie Recommendation System Web Application
This chapter applies the knowledge developed in the previous chapters to implement a movie recommendation system using django and the
recommendation techniques reviewed in the previous chapters. In particular, the implemented system supports an information retrieval system (Term Frequency, Inverse Document Frequency (TF-IDF) model) to allow the user to find movies typing some relevant words, together with a recommendation system (based on CF item-based and log-likelihood ratio).
Chapter 8, Sentiment Analyser Application for Movie Reviews
This chapter is about the creation of a web application for movie reviews sentiment analysis. It employs data mining techniques to extract information related to movies from the web and perform sentiment analysis. The web crawling part is performed through the use of the scrapy library using the bing REST API. Natural language processing is performed through nltk: tokenization, selection of the most important words and bigrams, and classification using a Naive Bayes classifier. The fetched reviews are ranked according to the usual PageRank algorithm.
The book provides an extensive analytical index, but is missing a bibliography (although papers and online documentation are frequently referenced in the text).
...
Conclusions: this book gives a complete overview of the more important algorithms in the field of machine learning, and shows how to employ them in a typical production scenario. Given the complexity of the topics, it's probably more useful for the technician already familiar with the machine learning theory. Indeed, although the theory is shortly exposed in the first chapters, it assumes already some familiarity on the subject, so this cannot be considered an introduction to machine learning concepts and mathematical/statistical models.
If you are already somehow acquainted with machine learning theory and statistical methods, and want to know how to apply them to build a complete web application using the state-of-the-art tools and Python libraries, this book is a perfect and precious choice. Particularly appreciated is the care committed on providing extensive examples, which cover most if not all the algorithms reviewed in the book, and can be adopted as the foundation to implement similar machine learning systems based on Python.
Even in the case you are not a technician or a student of the subject, but you are simply curious about machine learning and/or in case you want to widen the range of your skill-set and knowledge to venture in a new expanding scientific/technological field, given its raising importance in technology and society, this book is a recommended reading, together with some more focused formation on its mathematical/statistical background.
0 of 0 people found the following review helpful.
Good introduction to machine learning in python
By Roberto Congiu
This book is a good introduction to machine learning that mixes a theoretical overview of the most used machine learning algorithms with a step by step guide on how to integrate machine learning in simple web applications, using the Django framework.
All the code and examples are in python, so I'd recommend this book if python is your language of choice.
I'd recommend this book if either you are a data scientist who's used to other tools (like R) and wants to learn how to build ML web apps in python, or if you're a python web developer who wants to learn machine learning. In this case, I'd recommend you also get a more theoretical book like Pattern Recognition and Machine Learning (Information Science and Statistics) .
The book goes over the different families of ML algorithms (supervised, unsupervised) showing the math behind it, and has code examples on how to use it in python. One slightly annoying thing on the PDF version is that the python code is printed as an image, so you can't copy and paste snippets from it.
Then it gives you a brief introduction to Django and how to build simple web applications with it. Finally, it merges the data science part with the web developing part, showing how to build a movie recommendation app with sentiment analysis. The Django part is just enough to get you started.
Things that the book does NOT cover is how to run these algorithms on massive datasets (aka big data), for which you'd need something like spark.
1 of 1 people found the following review helpful.
I highly recommend this book!
By Giovanni Pisani
This book is an excellent choice to fill the gap between Machine Learning and web development.
The use of Python, an easy to learn programming language, helps a lot in the comprehension and development of the algorithms. Chapter 1 is a very nice and concise beginning tutorial of the NumPy and Pandas Python modules.
Chapters 2 and 3 are also very useful for Machine Learning beginners like me. They encompass the most commonly used algorithms for unsupervised and supervised Machine Learning. Within the following chapters, the reader is pleasantly led across the main web data mining techniques, the most popular recommended systems used in a commercial environment, and finally into the Django framework to begin developing web applications.
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