Machine Learning for the Web
Seiten
2016
Packt Publishing Limited (Verlag)
978-1-78588-660-7 (ISBN)
Packt Publishing Limited (Verlag)
978-1-78588-660-7 (ISBN)
Explore the web and make smarter predictions using Python
About This Book
• 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.
Who This Book Is 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
In Detail
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.
Style and approach
Instead of being overwhelmed with multiple concepts at once, this book provides a step-by-step approach that will guide you through one topic at a time.
An intuitive step-by step guide that will focus on one key topic at a time. Building upon the acquired knowledge in each chapter, we will connect the fundamental theory and practical tips by illustrative visualizations and hands-on code examples.
About This Book
• 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.
Who This Book Is 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
In Detail
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.
Style and approach
Instead of being overwhelmed with multiple concepts at once, this book provides a step-by-step approach that will guide you through one topic at a time.
An intuitive step-by step guide that will focus on one key topic at a time. Building upon the acquired knowledge in each chapter, we will connect the fundamental theory and practical tips by illustrative visualizations and hands-on code examples.
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.
| Erscheinungsdatum | 13.08.2016 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-78588-660-6 / 1785886606 |
| ISBN-13 | 978-1-78588-660-7 / 9781785886607 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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