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Hands-On Machine Learning with Scikit-Learn and TensorFlow: Conce...
by Aurélien Géron

Language

English

Pages

576

Publication Date

March 13, 2017

Product Description
Customer Reviews
<div><p><b>Graphics in this book are printed in black and white</b>.</p><p>Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.</p><p>By using concrete examples, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.</p><ul><li>Explore the machine learning landscape, particularly neural nets</li><li>Use scikit-learn to track an example machine-learning project end-to-end</li><li>Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods</li><li>Use the TensorFlow library to build and train neural nets</li><li>Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning</li><li>Learn techniques for training and scaling deep neural nets</li><li>Apply practical code examples without acquiring excessive machine learning theory or algorithm details</li></ul></div>
CompTIA A+ Certification All-in-One Exam Guide, Ninth Edition (Ex...
by Mike Meyers

Language

English

Pages

1508

Publication Date

January 07, 2016

Product Description
Customer Reviews
<h4>This bestselling on-the-job reference and test preparation guide has been fully revised for the new 2015 CompTIA exam objectives for exams 901 & 902</h4> <p>Written by the leading authority on CompTIA A+ certification and training, this self-study set has been thoroughly updated to cover 100% of the topics covered on the latest edition of the exam. New topics include managing and maintaining cellular devices, including tablets; configuring operating systems, including Windows 8, Android, and iOS; and enhanced, mobile-centered security and troubleshooting procedures. The <em>All-in-One Exam Guide</em> enables you to take the test with complete confidence. It also serves as a practical reference for IT support and technical personnel.</p> <p><strong>Bonus electronic content includes:</strong></p> <ul><li>Practice exams with hundreds of accurate questions</li> <li>More than an hour of video training featuring Mike Meyers</li> <li>Performance-based simulations that prepare you for the performance-based questions on the exam</li> <li>A collection of Mike's favorite free PC tools</li> </ul> <p><strong>Key Features include:</strong></p> <ul><li>Written with the “in the trenches” voice and clarity Mike Meyers is known for</li> <li>Features pre-assessment tests, exam tips, and “Try This!” sections to reinforce difficult topics</li> <li>Includes a coupon for 10% off of the exam fee, a $37 value</li> </ul>
Swipe to Unlock: A Primer on Technology and Business Strategy
by , Neel Mehta

Language

English

Pages

321

Publication Date

September 19, 2017

Product Description
Customer Reviews
<b>Authored by Product Managers at Google, Microsoft, and Facebook, </b><i>Swipe to Unlock</i> is a guide which enables anyone to understand the core concepts of tech and the business strategy behind it. <b>It is a must-read for anyone interested in product management, marketing, business strategy, or sales roles in the tech industry.</b><br /><br />This <b>#1 Amazon Business Bestseller</b> has been featured in <b>Forbes</b> and <b>Business Insider</b> and was touted as <b>"our generation's Rosetta Stone for enabling non-engineers to peer into the technology changing everyday life"</b> by Jeremy Schifeling.<br /><br />You probably search on Google every day. But have you ever wondered how Google makes billions of dollars while providing search, email, and maps for free? How do they figure out which ads perfectly capture your interests? And how do they search the entire internet so quickly, anyway?<br /><br />By answering real-world questions like this, <i>Swipe to Unlock</i> gives you a peek under the hood of the technology you use every day, decodes technologists' weirdest buzzwords, and shows you how technology is changing the society we live in for better or for worse. Unlock the answers you need to become a better-educated consumer, digital citizen, or technology professional.<br />
Don't Make Me Think, Revisited: A Common Sense Approach to Web Us...
by Steve Krug

Language

English

Pages

210

Publication Date

December 23, 2013

Product Description
Customer Reviews
Since<i> Don’t Make Me Think </i>was first published in 2000, hundreds of thousands of Web designers and developers have relied on usability guru Steve Krug’s guide to help them understand the principles of intuitive navigation and information design. Witty, commonsensical, and eminently practical, it’s one of the best-loved and most recommended books on the subject.<br /> <br />Now Steve returns with fresh perspective to reexamine the principles that made<i> Don’t Make Me Think</i> a classic–with updated examples and a new chapter on mobile usability. And it’s still short, profusely illustrated…and best of all–fun to read.<br /> <br />If you’ve read it before, you’ll rediscover what made <i>Don’t Make Me Think</i> so essential to Web designers and developers around the world. If you’ve never read it, you’ll see why so many people have said it should be required reading for anyone working on Web sites.<br /> <br /> <br />“After reading it over a couple of hours and putting its ideas to work for the past five years, I can say it has done more to improve my abilities as a Web designer than any other book.”<br />–Jeffrey Zeldman, author of Designing with Web Standards <p style="margin: 0px;"> </p>
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and ...
by Wes McKinney

Language

English

Pages

550

Publication Date

September 25, 2017

Product Description
Customer Reviews
<div><p>Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.</p><p>Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.</p><ul><li>Use the IPython shell and Jupyter notebook for exploratory computing</li><li>Learn basic and advanced features in NumPy (Numerical Python)</li><li>Get started with data analysis tools in the pandas library</li><li>Use flexible tools to load, clean, transform, merge, and reshape data</li><li>Create informative visualizations with matplotlib</li><li>Apply the pandas groupby facility to slice, dice, and summarize datasets</li><li>Analyze and manipulate regular and irregular time series data</li><li>Learn how to solve real-world data analysis problems with thorough, detailed examples</li></ul></div>
Data Science for Executives: Leveraging Machine Intelligence to D...
by Nir Kaldero

Language

English

Pages

184

Publication Date

October 10, 2018

Product Description
Customer Reviews
We are in the 4th industrial revolution; companies need to figure out how to survive.<br /> <br />In this exciting revolution, machine intelligence has had a more unprecedented impact on business than the internet, and it’s the only path to corporate survival in the future. In Data Science for Executives, Nir Kaldero dispels the myths and confusion surrounding this game-changing technology and provides practical strategies for harnessing its profitable power. This essential tome provides illuminating case studies, important guiding principles, and effective on-the-ground actions for incorporating machine intelligence into your organization and employing it to enhance your business though the wealth of data that flows into your business.<br /> <br />Leaders don’t have to be scientists to unlock the power of AI technology that is already radically altering the industrial landscape. If you’re ready to meet the challenges of this new revolution, this essential guide will help you take your business to the next level.<br />
Building Microservices: Designing Fine-Grained Systems
by Sam Newman

Language

English

Pages

280

Publication Date

February 02, 2015

Product Description
Customer Reviews
<div><p>Distributed systems have become more fine-grained in the past 10 years, shifting from code-heavy monolithic applications to smaller, self-contained microservices. But developing these systems brings its own set of headaches. With lots of examples and practical advice, this book takes a holistic view of the topics that system architects and administrators must consider when building, managing, and evolving microservice architectures.</p><p>Microservice technologies are moving quickly. Author Sam Newman provides you with a firm grounding in the concepts while diving into current solutions for modeling, integrating, testing, deploying, and monitoring your own autonomous services. You’ll follow a fictional company throughout the book to learn how building a microservice architecture affects a single domain.</p><ul><li>Discover how microservices allow you to align your system design with your organization’s goals</li><li>Learn options for integrating a service with the rest of your system</li><li>Take an incremental approach when splitting monolithic codebases</li><li>Deploy individual microservices through continuous integration</li><li>Examine the complexities of testing and monitoring distributed services</li><li>Manage security with user-to-service and service-to-service models</li><li>Understand the challenges of scaling microservice architectures</li></ul></div>
Python Machine Learning: Machine Learning and Deep Learning with ...
by , Vahid Mirjalili

Language

English

Pages

624

Publication Date

September 20, 2017

Product Description
Customer Reviews
<h4>Key Features</h4><ul><li>Second edition of the bestselling book on Machine Learning</li><li>A practical approach to key frameworks in data science, machine learning, and deep learning</li><li>Use the most powerful Python libraries to implement machine learning and deep learning</li><li>Get to know the best practices to improve and optimize your machine learning systems and algorithms</li></ul><h4>Book Description</h4><p>Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.</p><p>Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.</p><p>Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.</p><p>If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.</p><h4>What you will learn</h4><ul><li>Understand the key frameworks in data science, machine learning, and deep learning</li><li>Harness the power of the latest Python open source libraries in machine learning</li><li>Explore machine learning techniques using challenging real-world data</li><li>Master deep neural network implementation using the TensorFlow library</li><li>Learn the mechanics of classification algorithms to implement the best tool for the job</li><li>Predict continuous target outcomes using regression analysis</li><li>Uncover hidden patterns and structures in data with clustering</li><li>Delve deeper into textual and social media data using sentiment analysis</li></ul><h4>Table of Contents</h4><ol><li>Giving Computers the Ability to Learn from Data</li><li>Training Simple Machine Learning Algorithms for Classification</li><li>A Tour of Machine Learning Classifiers Using Scikit-Learn</li><li>Building Good Training Sets - Data Preprocessing</li><li>Compressing Data via Dimensionality Reduction</li><li>Learning Best Practices for Model Evaluation and Hyperparameter Tuning</li><li>Combining Different Models for Ensemble Learning</li><li>Applying Machine Learning to Sentiment Analysis</li><li>Embedding a Machine Learning Model into a Web Application</li><li>Predicting Continuous Target Variables with Regression Analysis</li><li>Working with Unlabeled Data - Clustering Analysis</li><li>Implementing a Multilayer Artificial Neural Network from Scratch</li><li>Parallelizing Neural Network Training with TensorFlow</li><li>Going Deeper - The Mechanics of TensorFlow</li><li>Classifying Images with Deep Convolutional Neural Networks</li><li>Modeling Sequential Data using Recurrent Neural Networks</li></ol>
The Self-Taught Programmer: The Definitive Guide to Programming P...
by Cory Althoff

Language

English

Pages

299

Publication Date

December 26, 2016

Product Description
Customer Reviews
I am a self-taught programmer. After a year of self-study, I learned to program well enough to land a job as a software engineer II at eBay. Once I got there, I realized I was severely under-prepared. I was overwhelmed by the amount of things I needed to know but hadn't learned yet. My journey learning to program, and my experience at my first job as a software engineer were the inspiration for this book. <br /><br />This book is not just about learning to program; although you will learn to code. If you want to program professionally, it is not enough to learn to code; that is why, in addition to helping you learn to program, I also cover the rest of the things you need to know to program professionally that classes and books don't teach you. "The Self-taught Programmer" is a roadmap, a guide to take you from writing your first Python program, to passing your first technical interview. I divided the book into six sections: <br /><br />1. Learn to program in Python 3 and build your first program. <br />2. Learn Object-oriented programming and create a powerful Python program to get you hooked. <br />3. Learn to use tools like Git, Bash, regular expressions and databases. Then use your new coding skills to build a web scraper. <br />4. Study Computer Science fundamentals including computer architecture, data structures, algorithms and network programming. <br />5. Learn to program for production: I cover the software development process, testing, and best coding practices.<br />6. Finish with tips for working with a team and landing a programming job.<br /><br />You CAN learn to program professionally. The path is there. Will you take it?
Practical Statistics for Data Scientists: 50 Essential Concepts
by , Andrew Bruce

Language

English

Pages

318

Publication Date

May 10, 2017

Product Description
Customer Reviews
<div><p>Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.</p><p>Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.</p><p>With this book, you’ll learn:</p><ul><li>Why exploratory data analysis is a key preliminary step in data science</li><li>How random sampling can reduce bias and yield a higher quality dataset, even with big data</li><li>How the principles of experimental design yield definitive answers to questions</li><li>How to use regression to estimate outcomes and detect anomalies</li><li>Key classification techniques for predicting which categories a record belongs to</li><li>Statistical machine learning methods that “learn” from data</li><li>Unsupervised learning methods for extracting meaning from unlabeled data</li></ul></div>

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