Building Machine Learning Systems with Python - Second Edition

Get more from your data through creating practical machine learning systems with Python About This BookBuild your own Python-based machine learning systems tailored to solve any problemDiscover how Python offers a multiple context solution for create machine learning systemsPractical scenarios using the key Python machine learning libraries to successfully implement in your projectsWho This Book Is ForThis book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems. What You Will LearnBuild a classification system that can be applied to text, images, or soundsUse NumPy, SciPy, scikit-learn scientific Python open source libraries for scientific computing and machine learningExplore the mahotas library for image processing and computer visionBuild a topic model for the whole of WikipediaEmploy Amazon Web Services to run analysis on the cloudDebug machine learning problemsGet to grips with recommendations using basket analysisRecommend products to users based on past purchasesIn DetailUsing 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. Youll 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. 10. Computer Vision -- Introducing image processing -- Loading and displaying images -- Thresholding -- Gaussian blurring -- Putting the center in focus -- Basic image classification -- Computing features from images -- Writing your own features -- Using features to find similar images -- Classifying a harder dataset -- Local feature representations -- Summary -- 11. Dimensionality Reduction -- Sketching our roadmap -- Selecting features -- Detecting redundant features using filters -- Correlation -- Mutual information -- Asking the model about the features using wrappers -- Other feature selection methods -- Feature extraction -- About principal component analysis -- Sketching PCA -- Applying PCA -- Limitations of PCA and how LDA can help -- Multidimensional scaling -- Summary -- 12. Bigger Data -- Learning about big data -- Using jug to break up your pipeline into tasks -- An introduction to tasks in jug -- Looking under the hood -- Using jug for data analysis -- Reusing partial results -- Using Amazon Web Services -- Creating your first virtual machines -- Installing Python packages on Amazon Linux -- Running jug on our cloud machine -- Automating the generation of clusters with StarCluster -- Summary -- A. Where to Learn More Machine Learning -- Online courses -- Books -- Question and answer sites -- Blogs -- Data sources -- Getting competitive -- All that was left out -- Summary -- Index

About This Book

• Build full-featured web applications, such as Spring MVC applications, efficiently that will get you up and running with Spring web development
• Reuse working code snippets handy for integration scenarios such as Twitter, e-mail, FTP, databases, and many others
• An advanced guide which includes Java programs to integrate Spring with Thymeleaf

Who This Book Is For

If you are a Java developer with experience in developing applications with Spring, then this book is perfect for you. A good working knowledge of Spring programming conventions and applying dependency injections is recommended to make the most of this book.