Building Machine Learning Systems with Python: RAW
|Also available on:|
- A practical, scenario-based tutorial to get into the right mind set of a machine learner (data exploration)
- Master the diverse ML Python libraries and start building your Python-based ML systems
- Wide and practical coverage of ML areas to immediately implement in your projects - Regression, Recommender Systems, Computer Vision, and much more
||IN THE BOOK|
|2||A typical Machine Learning workflow with the Iris data set||IN THE BOOK|
|3||Clustering - Finding related posts||IN THE BOOK|
|4||Topic Modeling - Finding topics||IN THE BOOK|
|5||Classification - Assigning experts to questions
||IN THE BOOK|
|6||Classification - Sentiment analysis
|7||Regression - Recommendations||IN THE BOOK|
|8||Regression - Recommendations improved||MAY 2013|
||Classification - Music genre classification
|10||Computer Vision - Task: Character recognition||MAY 2013|
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What you will learn from this book
- Get to grips with Machine Learning with Python and start building your own ML projects
- Trust numbers to measure success
- Build and use a classifier in Python
- Learn to build and use a topic model in Python
- Discover where machine learning can be used in typical web contexts
Machine Learning is a branch of Artificial Intelligence that focuses on prediction, based on known properties of the training data, and Python is the most used programming language when it comes to Machine Learning. This book is about Machine Learning programming in Python through practical and effective examples.
Machine Learning has become one of the most active and exciting areas of computer science research, largely due to its widespread applicability to problems as diverse as natural language processing, speech recognition, spam detection, search, computer vision, gene discovery, medical diagnosis, and robotics. At the same time, the growing popularity of the Internet and social networking sites like Facebook has led to the availability of novel sources of data on the preferences, behavior, and beliefs of massive populations of users.
"Building Machine Learning system with Python" shows you exactly how to find patterns through raw data. The book starts by brushing up your Python ML knowledge and introducing libraries and then moving on to more serious projects on datasets, modeling, recommendations, and improving recommendations through examples.
The wide applicability of Python ML is an important factor. More and more, machine learning is used to enhance different applications.
Python is uniquely positioned to be used as an application language (either on the desktop or on the web) and to expose the computationally intensive learning algorithms in an easy-to-use way.
There is a lot of demand for machine learning in the industry, and there are several high quality open source Machine Learning libraries for Python. This book shows you how to stay ahead by discussing cutting-edge Python ML aspects such as regression and classification, and then moving to more advanced areas of improving your recommendations, music genre classification, character recognition, and so on.
This book is currently available as a RAW (Read As we Write) book. A RAW book is an ebook, and this one is priced at 20% off the usual eBook price. Once you purchase the RAW book, you can immediately download the content of the book so far, and when new chapters become available, you will be notified, and can download the new version of the book. When the book is published, you will receive the full, finished eBook.
If you like, you can preorder the print book at the same time as you purchase the RAW book at a significant discount.
Since a RAW book is an eBook, a RAW book is non returnable and non refundable.
Local taxes may apply to your eBook purchase.
This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.
Who this book is for
This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to provide Machine Learning support to their existing projects, and see them get implemented effectively .
Computer science researchers, data scientists, Artificial Intelligence programmers, and statistical programmers would equally gain from this book and would learn about effective implementation through lots of the practical examples discussed.
Readers need no prior experience with Machine Learning or statistical processing. Python development experience is assumed.