Machine Learning for Hackers

Case Studies and Algorithms to Get You Started

Author: Drew Conway,John Myles White

Publisher: "O'Reilly Media, Inc."

ISBN: 1449330533

Category: Computers

Page: 324

View: 3861

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If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research. Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text Use linear regression to predict the number of page views for the top 1,000 websites Learn optimization techniques by attempting to break a simple letter cipher Compare and contrast U.S. Senators statistically, based on their voting records Build a “whom to follow” recommendation system from Twitter data
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Cool Careers Without College for People Who Love Coding

Author: Asher Powell

Publisher: The Rosen Publishing Group, Inc

ISBN: 1508175373

Category: Juvenile Nonfiction

Page: 112

View: 6969

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This comprehensive career guide helps readers take a close look at coding as a career path and gives them a long, actionable list for turning their interests into a career across a variety of fields. Whether a student is interested in games, engineering, design, or systems administration, each career path comes with a detailed list of resources and first-person accounts from professionals in the field. This guide is all a coding enthusiast needs to get started planning and building a career, all without having to worry about student loans.
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Machine Learning with R

Author: Brett Lantz

Publisher: Packt Publishing Ltd

ISBN: 1782162151

Category: Computers

Page: 396

View: 2589

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Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
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Hands-On Machine Learning for Cybersecurity

Safeguard your system by making your machines intelligent using the Python ecosystem

Author: Soma Halder,Sinan Ozdemir

Publisher: Packt Publishing Ltd

ISBN: 178899096X

Category: Computers

Page: 318

View: 5852

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Get into the world of smart data security using machine learning algorithms and Python libraries Key Features Learn machine learning algorithms and cybersecurity fundamentals Automate your daily workflow by applying use cases to many facets of security Implement smart machine learning solutions to detect various cybersecurity problems Book Description Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems What you will learn Use machine learning algorithms with complex datasets to implement cybersecurity concepts Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes Use TensorFlow in the cybersecurity domain and implement real-world examples Learn how machine learning and Python can be used in complex cyber issues Who this book is for This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book
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Machine Learning for Computer and Cyber Security

Principle, Algorithms, and Practices

Author: Brij B. Gupta,Quan Z. Sheng

Publisher: CRC Press

ISBN: 0429995717

Category: Computers

Page: 352

View: 5252

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While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques. This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals. Key Features: This book contains examples and illustrations to demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security. It showcases important security aspects and current trends in the field. It provides an insight of the future research directions in the field. Contents of this book help to prepare the students for exercising better defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation using machine learning based approaches in better manner.
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Business Analytics Using R - A Practical Approach

Author: Umesh R Hodeghatta,Umesha Nayak

Publisher: Apress

ISBN: 1484225147

Category: Computers

Page: 280

View: 1624

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Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. What You Will Learn • Write R programs to handle data • Build analytical models and draw useful inferences from them • Discover the basic concepts of data mining and machine learning • Carry out predictive modeling • Define a business issue as an analytical problem Who This Book Is For Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.
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The SEO Battlefield

Winning Strategies for Search Marketing Programs

Author: Anne Ahola Ward

Publisher: "O'Reilly Media, Inc."

ISBN: 1491958340

Category: Business & Economics

Page: 204

View: 3276

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Welcome to the battlefield. Every day, companies ranging from startups to enterprises fight to achieve high search rankings, knowing that previous success can quickly vanish. With this practical guide, you’ll learn how to put search engine optimization (SEO) methodology into practice, including the research, data analysis, and constant experimentation required to build an SEO program specific to your organization that can help you improve search results. Running a successful SEO program requires a team with a mix of skills, including marketing, analytics, website development, and automation. Author Anne Ahola Ward walks marketers and developers through SEO essentials and provides real-world case studies of successful and not-so-successful SEO programs. You’ll quickly understand why this is both an exciting and critical time to adopt SEO in your organization. Perform keyword goals and research, and spot search trends Understand the motivation and creativity of the SEO mindset Run a campaign to generate traffic and measure the results Use mobile and platform-agnostic strategies for search growth Demonstrate the value of your search marketing efforts Include web development in your SEO program—everything from quick updates to UX/UI strategy Learn the sweetest way to run and report on an search program
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