mastering feature engineering principles and techniques for data scientists early release

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Mastering Feature Engineering

Author : Alice Zheng
ISBN : 1491953241
Genre : Computers
File Size : 45. 18 MB
Format : PDF, Kindle
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Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. To help fill the information gap on feature engineering, this complete hands-on guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic. Author Alice Zheng explains common practices and mathematical principles to help engineer features for new data and tasks. If you understand basic machine learning concepts like supervised and unsupervised learning, you're ready to get started. Not only will you learn how to implement feature engineering in a systematic and principled way, you'll also learn how to practice better data science. Learn exactly what feature engineering is, why it's important, and how to do it well Use common methods for different data types, including images, text, and logs Understand how different techniques such as feature scaling and principal component analysis work Understand how unsupervised feature learning works in the case of deep learning for images

Feature Extraction

Author : Isabelle Guyon
ISBN : 9783540354888
Genre : Computers
File Size : 72. 54 MB
Format : PDF, Docs
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This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons.

Mastering Predictive Analytics With R

Author : Rui Miguel Forte
ISBN : 9781783982813
Genre : Computers
File Size : 90. 4 MB
Format : PDF, Kindle
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R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. This book is designed to be both a guide and a reference for moving beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models and the predictive modeling process. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real world data sets. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world data sets and mastered a diverse range of techniques in predictive analytics.

Mastering Bitcoin

Author : Andreas M. Antonopoulos
ISBN : 9781491921982
Genre : Computers
File Size : 32. 26 MB
Format : PDF, Docs
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Want to join the technological revolution that’s taking the world of finance by storm? Mastering Bitcoin is your guide through the seemingly complex world of bitcoin, providing the requisite knowledge to help you participate in the internet of money. Whether you’re building the next killer app, investing in a startup, or simply curious about the technology, this practical book is essential reading. Bitcoin, the first successful decentralized digital currency, is still in its infancy and it’s already spawned a multi-billion dollar global economy. This economy is open to anyone with the knowledge and passion to participate. Mastering Bitcoin provides you with the knowledge you need (passion not included). This book includes: A broad introduction to bitcoin—ideal for non-technical users, investors, and business executives An explanation of the technical foundations of bitcoin and cryptographic currencies for developers, engineers, and software and systems architects Details of the bitcoin decentralized network, peer-to-peer architecture, transaction lifecycle, and security principles Offshoots of the bitcoin and blockchain inventions, including alternative chains, currencies, and applications User stories, analogies, examples, and code snippets illustrating key technical concepts

A Survey Of Statistical Network Models

Author : Anna Goldenberg
ISBN : 9781601983206
Genre : Computers
File Size : 90. 82 MB
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"Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active "network community" and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online "networking communities" such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data. Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics."--Pages 7-8.

Python For Data Analysis

Author : Wes McKinney
ISBN : 9781449319793
Genre : Computers
File Size : 77. 34 MB
Format : PDF, ePub
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Presents case studies and instructions on how to solve data analysis problems using Python.

Python Algorithms

Author : Magnus Lie Hetland
ISBN : 9781430232377
Genre : Computers
File Size : 28. 97 MB
Format : PDF, Kindle
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Python Algorithms explains the Python approach to algorithm analysis and design. Written by Magnus Lie Hetland, author of Beginning Python, this book is sharply focused on classical algorithms, but it also gives a solid understanding of fundamental algorithmic problem-solving techniques. The book deals with some of the most important and challenging areas of programming and computer science, but in a highly pedagogic and readable manner. The book covers both algorithmic theory and programming practice, demonstrating how theory is reflected in real Python programs. Well-known algorithms and data structures that are built into the Python language are explained, and the user is shown how to implement and evaluate others himself. What you’ll learn Transform new problems to well-known algorithmic problems with efficient solutions, or show that the problems belong to classes of problems thought not to be efficiently solvable. Analyze algorithms and Python programs both using mathematical tools and basic experiments and benchmarks. Prove correctness, optimality, or bounds on approximation error for Python programs and their underlying algorithms. Understand several classical algorithms and data structures in depth, and be able to implement these efficiently in Python. Design and implement new algorithms for new problems, using time-tested design principles and techniques. Speed up implementations, using a plethora of tools for high-performance computing in Python. Who this book is for The book is intended for Python programmers who need to learn about algorithmic problem-solving, or who need a refresher. Students of computer science, or similar programming-related topics, such as bioinformatics, may also find the book to be quite useful. Table of Contents Introduction The Basics Counting 101 Induction and Recursion ... and Reduction Traversal: The Skeleton Key of Algorithmics Divide, Combine, and Conquer Greed Is Good? Prove It! Tangled Dependencies and Memoization From A to B with Edsger and Friends Matchings, Cuts, and Flows Hard Problems and (Limited) Sloppiness

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