Hacker S Guide To Machine Learning Concepts
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Hacker’s Guide to Machine Learning Concepts
Hacker’s Guide to Machine Learning Concepts is crafted for those eager to dive into the world of ethical hacking. This book demonstrates how ethical hacking can help companies identify and fix vulnerabilities efficiently. With the rise of data and the evolving IT industry, the scope of ethical hacking continues to expand. We cover various hacking techniques, identifying weak points in programs, and how to address them. The book is accessible even to beginners, offering chapters on machine learning and programming in Python. Written in an easy-to-understand manner, it allows learners to practice hacking steps independently on Linux or Windows systems using tools like Netsparker. This book equips you with fundamental and intermediate knowledge about hacking, making it an invaluable resource for learners.
Machine Learning for Hackers
Author: Drew Conway
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2012-02-13
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
The Hacker's Guide to Scaling Python
Python is a wonderful programming language that allows writing applications quickly. But how do you make those applications scale for thousands of users and requests? It takes years of practice, research, trial and errors to build experience and knowledge along the way. Simple questions such as "How do I make my code faster?" or "How do I make sure there is no bottleneck?" cost hours to find good answers. Without enough background on the topic, you'll never be sure that any answer you'll come up with will be correct. The Hacker's Guide to Scaling Python will help you solve that by providing guidelines, tips and best practice. Adding a few interviews of experts on the subject, you will learn how you can distribute your Python application so it is able to process thousands of requests.