Building Machine Learning Systems with Python - Second Edition

This 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. Abstract: This 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 Content: Cover Copyright Credits About the Authors About the Reviewers www.PacktPub.com Table of Contents Preface Chapter 1: Getting Started with Python Machine Learning Machine learning and Python -- a dream team What the book will teach you (and what it will not) What to do when you are stuck Getting started Introduction to NumPy, SciPy, and matplotlib Installing Python Chewing data efficiently with NumPy and intelligently with SciPy Learning NumPy Indexing Handling nonexisting values Comparing the runtime Learning SciPy Our first (tiny) application of machine learning. Reading in the dataPreprocessing and cleaning the data Choosing the right model and learning algorithm Before building our first model ... Starting with a simple straight line Towards some advanced stuff Stepping back to go forward -- another look at our data Training and testing Answering our initial question Summary Chapter 2: Classifying with Real-world Examples The Iris dataset Visualization is a good first step Building our first classification model Evaluation -- holding out data and cross-validation Building more complex classifiers. A more complex dataset and a more complex classifierLearning about the Seeds dataset Features and feature engineering Nearest neighbor classification Classifying with scikit-learn Looking at the decision boundaries Binary and multiclass classification Summary Chapter 3: Clustering -- Finding Related Posts Measuring the relatedness of posts How not to do it How to do it Preprocessing -- similarity measured as a similar number of common words Converting raw text into a bag of words Counting words Normalizing word count vectors Removing less important words Stemming. Stop words on steroidsOur achievements and goals Clustering K-means Getting test data to evaluate our ideas on Clustering posts Solving our initial challenge Another look at noise Tweaking the parameters Summary Chapter 4: Topic Modeling Latent Dirichlet allocation Building a topic model Comparing documents by topics Modeling the whole of Wikipedia Choosing the number of topics Summary Chapter 5: Classification -- Detecting Poor Answers Sketching our roadmap Learning to classify classy answers Tuning the instance Tuning the classifier Fetching the data. Slimming the data down to chewable chunksPreselection and processing of attributes Defining what is a good answer Creating our first classifier Starting with kNN Engineering the features Training the classifier Measuring the classifier's performance Designing more features Deciding how to improve Bias-variance and their tradeoff Fixing high bias Fixing high variance High bias or low bias Using logistic regression A bit of math with a small example Applying logistic regression to our post classification problem Looking behind accuracy -- precision and recall. 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.