Luis Pedro Coelho

Building Machine Learning Systems with Python – Second Edition

Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learning libraries you can focus on the task at hand while being able to quickly try out many ideas.
This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and introducing libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling, creating recommendation systems. Later on, the book covers advanced topics such as topic modeling, basket analysis, and cloud computing. These will extend your abilities and enable you to create large complex systems.
With this book, you gain the tools and understanding required to build your own systems, tailored to solve your real-world data analysis problems.
541 trykte sider
Har du allerede læst den? Hvad synes du om den?


    aakononoffhar delt en vurderingfor 6 år siden
    👍Værd at læse

    Книга знакомит с основными концепциями машинного обучения. Так же есть примеры работы с jug - фреймворк для выполнения задач в нескольких потоках, AWS - сервис от амазона.


    Zaur Huseynovhar citeretfor 3 år siden
    But before you go there, you will have to define what you actually mean by "better". SciKit has a complete package dedicated only to this definition. The package is called sklearn.metrics and also contains a full range of different metrics to measure clustering quality. Maybe that should be the first place to go now. Right into the sources of the metrics package.
    Zaur Huseynovhar citeretfor 3 år siden
    SciKit provides a wide range of clustering approaches in the sklearn.cluster package. You can get a quick overview of advantages and drawbacks of each of them at
    Zaur Huseynovhar citeretfor 3 år siden
    UCI Machine Learning Dataset Repository

    The University of California at Irvine (UCI) maintains an online repository of machine learning datasets (at the time of writing, they list 233 datasets). Both the Iris and the Seeds dataset used in this chapter were taken from there.

    The repository is available online at

På boghylderne

    Ethan Hunt
    • 82
    • 95
    Антон Панченко
    ML (machine learning)
    • 9
    • 18
    Andy Bitt
    • 19
    • 4
    Oleg Danilchenko
    Machine learning
    • 15
    • 4
Træk og slip dine filer (ikke mere end 5 ad gangen)