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"Machine learning focuses on predition-- using what you know to predict what you would like to know based on historical relationships between the two. At its core, it's a mathematical/algorithm-based technology that, until recently, required a deep understanding of math and statistical concepts, and fluency in R and other specialized languages. "Machine learning with Spark and Python" simplifies machine learning for a broader audience and wider application by focusing on two algorithm families that effectively predict outcomes, and by showing you how to apply them using the popular and accessible Python programming language. This edition shows how pyspark extends these two algorithms to extremely large data sets requiring multiple distributed processors. The same basic concepts apply. Author Michael Bowles draws from years of machine learning expertise to walk you through the design, construction, and implementation of your own machine learning solutions. The algorithms are explained in simple terms with no complex math, and sample code is provided to help you get started right away. You'll delve deep into the mechanisms behind the constructs, and learn how to select and apply the algorithm that will best solve the problem at hand, whether simple or complex. Detailed examples illustrate the machinery with specific, hackable code, and descriptive coverage of penalized linear regression and ensemble methods helps you understand the fundamental processes at work in machine learning. The methods are effective and well tested, and the results speak for themselves"

"With Early Release ebooks, you get books in their earliest form--the author's raw and unedited content as he or she writes--so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released. The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this comprehensive cookbook, data scientists and software engineers familiar with Python will benefit from almost 200 practical recipes for building a comprehensive machine-learning pipeline--everything from data preprocessing and feature engineering to model evaluation and deep learning. Learn from author Chris Albon, a data scientist who has written more than 500 tutorials on Python, data science, and machine learning. Each recipe in this practical cookbook includes code solutions that you can put to work right away, along with a discussion of how and why they work--making it ideal as a learning tool and reference book"

"This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras.This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming."

"Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process.Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. You will: Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production"

"In line with rapid business process digitalization in the Directorate General of Taxes, the size of the data stored in the institution has grown exponentially. However, there is a problem with generating value out of the valuable data assets. Correspondingly, this research provides machine-learning-based predictive analytics as a solution to the question of how to use taxpayers' trigger data as a decision support system to discover and realize unexplored tax potential. More specifically, this research presents predictive analytics models that can accurately predict which potential taxpayers are likely to pay their due. We developed three machine learning models: logistic regression, random forest, and decision tree. We analyzed 5,562 tax revenue potential data samples with eight predictors: trigger data nominal value, distance to tax office, type of taxpayer, media of tax report, type of tax, report status, registered year of taxpayer, and area coverage. Our study shows that the random forest model provided the best prediction performance. The resultant weight of each attribute indicated that the status of the tax report was the top tier of variable importance in predicting tax revenue potential. The analytics can help tax officers determine potential taxpayers with the highest likelihood to pay their due. Given the size of the data records, this approach can provide tax administrators with a powerful tool to increase work efficiency, combat tax evasion, and provide better customer service."

"Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race."

"The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise."

"Just like electricity, Machine Learning will revolutionize our life in many ways-some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with."