Build a classification system that can be applied to text, images, or sounds, Use scikit-learn, a Python open-source library for machine learning, Explore the mahotas library for image processing and computer vision, etc. with Building Machine Learning Systems with Python.
Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.
Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail.
Review:
Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques
Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on.
Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text’s most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.
Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.
Python has an excellent ecosystem of libraries for Machine Learning. The libraries are all well-documented but sometimes it is hard to figure out how to solve a problem end-to-end using one or more of these libraries. This book attempts to fill that niche. It contains 12 chapters, each focusing on one or two ML problems, and shows how an expert ML practitioner would build and evaluate solutions for these problems. The main focus of the book is on the famous Scikits-Learn library, along with its dependencies Numpy and Scipy, but there is also coverage of gensim (for topic modeling), mahotas (for image processing), jug and starcluster (for distributed computing). The tone of the book is very practical and hands-on, in the rare cases where theory is explained, it is done without math. At the same time, the book is much more than just an introduction to Python ML libraries - you will come away learning "insider secrets" that you can do to improve your solution and which are already available as API calls within one of these libraries.
This book presents application of algorithms to real world problems from machine learning perspective. It demonstrates practical examples of solving machine learning issues with Python scripts. Analysis and reasoning follows the examples. It guides the readers with simple algorithms and extends to the more complex machine learning issues. Whether you are a software professional or non technical person, this book will serve as an introductory material to the world of machine learning.
Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.
Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail.
Review:
Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques
Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on.
Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text’s most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.
Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.
Python has an excellent ecosystem of libraries for Machine Learning. The libraries are all well-documented but sometimes it is hard to figure out how to solve a problem end-to-end using one or more of these libraries. This book attempts to fill that niche. It contains 12 chapters, each focusing on one or two ML problems, and shows how an expert ML practitioner would build and evaluate solutions for these problems. The main focus of the book is on the famous Scikits-Learn library, along with its dependencies Numpy and Scipy, but there is also coverage of gensim (for topic modeling), mahotas (for image processing), jug and starcluster (for distributed computing). The tone of the book is very practical and hands-on, in the rare cases where theory is explained, it is done without math. At the same time, the book is much more than just an introduction to Python ML libraries - you will come away learning "insider secrets" that you can do to improve your solution and which are already available as API calls within one of these libraries.
This book presents application of algorithms to real world problems from machine learning perspective. It demonstrates practical examples of solving machine learning issues with Python scripts. Analysis and reasoning follows the examples. It guides the readers with simple algorithms and extends to the more complex machine learning issues. Whether you are a software professional or non technical person, this book will serve as an introductory material to the world of machine learning.
