Building machine learning systems with Python : master the art of machine learning with Python and build effective machine learning sytems with this intensive hands-on guide

Master the art of machine learning with Python and build effective machine learning systems with this intensive hands-on guide **Overview** * Master Machine Learning using a broad set of Python libraries and start building your own Python-based ML systems. * Covers classification, regression, feature engineering, and much more guided by practical examples. * A scenario-based tutorial to get into the right mind-set of a machine learner (data exploration) and successfully implement this in your new or existing projects. **In Detail** 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. 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. **What you will learn from this book** * 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 * Build a topic model of the whole of Wikipedia * Get to grips with recommendations using the basket analysis * Use the Jug package for data analysis * Employ Amazon Web Services to run analyses on the cloud * Recommend products to users based on past purchases **Approach** A practical, scenario-based tutorial, this book will help you get to grips with machine learning with Python and start building your own machine learning projects. 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. 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 – the 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 non-existing values Comparing runtime behaviors Learning SciPy Our first (tiny) machine learning application Reading in the data Preprocessing 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: Learning How to Classify with Real-world Examples The Iris dataset The first step is visualization Building our first classification model Evaluation – holding out data and cross-validation Building more complex classifiers A more complex dataset and a more complex classifier Learning about the Seeds dataset Features and feature engineering Nearest neighbor classification 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 similar number of common words Converting raw text into a bag-of-words Counting words Normalizing the word count vectors Removing less important words Stemming Installing and using NLTK Extending the vectorizer with NLTK's stemmer Stop words on steroids Our achievements and goals Clustering KMeans 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 (LDA) Building a topic model Comparing similarity in topic space 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 chunks Preselection and processing of attributes Defining what is a good answer Creating our first classifier Starting with the k-nearest neighbor (kNN) algorithm Engineering the features Training the classifier Measuring the classifier's performance Designing more features Deciding how to improve Bias-variance and its trade-off 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 postclassification problem Looking behind accuracy – precision and recall Slimming the classifier Ship it! Summary Chapter 6: Classification II – Sentiment Analysis Sketching our roadmap Fetching the Twitter data Introducing the Naive Bayes classifier Getting to know the Bayes theorem Being naive Using Naive Bayes to classify Accounting for unseen words and other oddities Accounting for arithmetic underflows Creating our first classifier and tuning it Solving an easy problem first Using all the classes Tuning the classifier's parameters Cleaning tweets Taking the word types into account Determining the word types Successfully cheating using SentiWordNet Our first estimator Putting everything together Summary Chapter 7: Regression – Recommendations Predicting house prices with regression Multidimensional regression Cross-validation for regression Penalized regression L1 and L2 penalties Using Lasso or Elastic nets in scikit-learn P greater than N scenarios An example based on text Setting hyperparameters in a smart way Rating prediction and recommendations Summary Chapter 8: Regression – Recommendations Improved Improved recommendations Using the binary matrix of recommendations Looking at the movie neighbors Combining multiple methods Basket analysis Obtaining useful predictions Analyzing supermarket shopping baskets Association rule mining More advanced basket analysis Summary Chapter 9: Classification III – Music Genre Classification Sketching our roadmap Fetching the music data Converting into a wave format Looking at music Decomposing music into sine wave components Using FFT to build our first classifier Increasing experimentation agility Training the classifier Using the confusion matrix to measure accuracy in multiclass problems An alternate way to measure classifier performance using receiver operator characteristic (ROC) Improving classification performance with Mel Frequency Cepstral Coefficients Summary Chapter 10: Computer Vision – Pattern Recognition Introducing image processing Loading and displaying images Basic image processing Thresholding Gaussian blurring Filtering for different effects Adding salt and pepper noise Putting the center in focus Pattern recognition Computing features from images Writing your own features Classifying a harder dataset Local feature representations Summary Chapter 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 (PCA) Sketching PCA Applying PCA Limitations of PCA and how LDA can help Multidimensional scaling (MDS) Summary Chapter 12: Big(ger) Data Learning about big data Using jug to break up your pipeline into tasks About tasks Reusing partial results Looking under the hood Using jug for data analysis Using Amazon Web Services (AWS) Creating your first machines Installing Python packages on Amazon Linux Running jug on our cloud machine Automating the generation of clusters with starcluster Summary Appendix: Where to Learn More about Machine Learning Online courses Books Q&A sites Blogs Data sources Getting competitive What was left out Summary Index

In Detail

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.

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.

Approach

A practical, scenario-based tutorial, this book will help you get to grips with machine learning with Python and start building your own machine learning projects. 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 is for Python programmers who are beginners in machine learning, but want to learn Machine learning. Readers are expected to know Python and be able to install and use open-source libraries. They are not expected to know machine learning, although the book can also serve as an introduction to some Python libraries for readers who know machine learning. This book does not go into the detail of the mathematics behind the algorithms.

This book primarily targets Python developers who want to learn and build machine learning in their projects, or who want to provide machine learning support to their existing projects, and see them getting implemented effectively.

As the Big Data explosion continues at an almost incomprehensible rate, being able to understand and process it becomes even more challenging. With Building Machine Learning Systems with Python, you'll learn everything you need to tackle the modern data deluge - by harnessing the unique capabilities of Python and its extensive range of numerical and scientific libraries, you will be able to create complex algorithms that can 'learn' from data, allowing you to uncover patterns, make predictions, and gain a more in-depth understanding of your data. Featuring a wealth of real-world examples, this book provides gives you with an accessible route into Python machine learning. Learn the Iris dataset, find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity. Dig deeper into machine learning, and discover guidance on classification and regression, with practical machine learning projects outlining effective strategies for sentiment analysis and basket analysis. The book also takes you through the latest in computer vision, demonstrating how image processing can be used for pattern recognition, as well as showing you how to get a clearer picture of your data and trends by using dimensionality reduction. Keep up to speed with one of the most exciting trends to emerge from the world of data science and dig deeper into your data with Python with this unique data science tutorial. 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.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. 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. This book primarily targets Python developers who want to learn about and build Machine Learning into their projects, or who want to pro