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Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you...

TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning
TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects...

Machine Learning and Security: Protecting Systems with Data and Algorithms
Machine Learning and Security: Protecting Systems with Data and Algorithms

Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you’ll explore ways to apply machine learning to security...

Communicating Science: A Practical Guide for Engineers and Physical Scientists
Communicating Science: A Practical Guide for Engineers and Physical Scientists

Read this book before you write your thesis or journal paper! Communicating Science is a textbook and reference on scientific writing oriented primarily at researchers in the physical sciences and engineering. It is written from the perspective of an experienced researcher. It draws on the authors' experience of teaching and working with...

Deep Learning with Python: A Hands-on Introduction
Deep Learning with Python: A Hands-on Introduction
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...
Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
Docker for Data Science: Building Scalable and Extensible Data Infrastructure Around the Jupyter Notebook Server
Learn Docker "infrastructure as code" technology to define a system for performing standard but non-trivial data tasks on medium- to large-scale data sets, using Jupyter as the master controller.

It is not uncommon for a real-world data set to fail to be easily managed. The set may not fit well into access memory or
...
Dark Web: Exploring and Data Mining the Dark Side of the Web (Integrated Series in Information Systems)
Dark Web: Exploring and Data Mining the Dark Side of the Web (Integrated Series in Information Systems)

The University of Arizona Artificial Intelligence Lab (AI Lab) Dark Web project is a long-term scientific research program that aims to study and understand the international terrorism (Jihadist) phenomena via a computational, data-centric approach. We aim to collect "ALL" web content generated by international terrorist groups,...

Numerical and Analytical Methods for Scientists and Engineers, Using Mathematica
Numerical and Analytical Methods for Scientists and Engineers, Using Mathematica
Up to this point in your career you have been asked to use mathematics to solve rather elementary problems in the physical sciences. However, when you graduate and become a working scientist or engineer you will often be confronted with complex real-world problems. Understanding the material in this book is a first step toward...
Solving Nonlinear Partial Differential Equations with Maple and Mathematica
Solving Nonlinear Partial Differential Equations with Maple and Mathematica

The emphasis of the book is given in how to construct different types of solutions (exact, approximate analytical, numerical, graphical) of numerous nonlinear PDEs correctly, easily, and quickly. The reader can learn a wide variety of techniques and solve numerous nonlinear PDEs included and many other differential equations, simplifying...

Python Machine Learning Case Studies: Five Case Studies for the Data Scientist
Python Machine Learning Case Studies: Five Case Studies for the Data Scientist
Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problems. The book uses a hands-on case study-based approach to crack real-world applications to which machine learning concepts can be applied. These smarter machines will enable your business processes to achieve efficiencies on...
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools

Summary

Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science.

...

Learning Spark: Lightning-Fast Big Data Analysis
Learning Spark: Lightning-Fast Big Data Analysis

Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java,...

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