“…—Bjarne Stroustrup, Designer of C++ “I am happy to see the content of Alex’s course, the development and teaching of which I strongly supported as the CTO of Silicon Graphics, now available to all programmers in this elegant little book.” —Forest Baskett, General Partner, New Enterprise Associates “Paul’s patience and architectural experience helped to organize Alex’s mathematical approach into a tightly-structured edifice—an impressive feat!” …”

“…Coverage includes Assessing the strengths and limitations of mainstream monitoring tools and IDS technologies Using Attack Graphs to map paths of network vulnerability and becoming more proactive about preventing intrusions Analyzing network behavior to immediately detect polymorphic worms, zero-day exploits, and botnet DoS attacks Understanding the theory, advantages, and disadvantages of the latest Web Application Firewalls Implementing IDS/IPS systems that protect wireless data traffic Enhancing your intrusion detection efforts by converging with physical security defenses Identifying attackers’ “geographical fingerprints” and using that information to respond more effectively Visualizing data traffic to identify suspicious patterns more quickly Revisiting intrusion detection ROI in light of new threats, compliance risks, and technical alternatives Includes contributions from these leading network security experts: Jeff Forristal, a.k.a. Rain Forest Puppy, senior security professional and creator of libwhisker Seth Fogie, CEO, Airscanner USA; leading-edge mobile security researcher; coauthor of Security Warrior Dr. …”

“…We will quickly go through the architecture and fundamentals of Active Directory and then dive deep into the core components, such as forests, domains, sites, trust relationships, OU, objects, attributes, DNS, and replication. …”

“…You also explore the different post-installation tasks, including configuration of domains and forests; management of users, computers, groups, and organizational units; and configuration of group policies. …”

“…In Norway, the outfields [utmark] are understood as mainly un-cultivated countryside areas such as forest and upland and make up over 70 per cent of the country’s land area. …”

“…What you will learn Prepare data for machine learning methods with ease Understand how to write production-ready code and package it for use Produce simple and effective data visualizations for improved insights Master advanced methods, such as Boosted Trees and deep neural networks Use natural language processing to extract insights in relation to text Implement tree-based classifiers, including Random Forest and Boosted Tree Who this book is for This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement..…”

“…You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. …”

“…Korstian Professor of Forest Economics & Management Nicholas School of the Environment, Duke University, USA…”

“…What You Will Learn Explore and make effective use of OpenCV's machine learning module Learn deep learning for computer vision with Python Master linear regression and regularization techniques Classify objects such as flower species, handwritten digits, and pedestrians Explore the effective use of support vector machines, boosted decision trees, and random forests Get acquainted with neural networks and Deep Learning to address real-world problems Discover hidden structures in your data using k-means clustering Get to grips with data pre-processing and feature engineering In Detail Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. …”

“…You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. …”

“…What you will learn Develop a joke recommendation engine to show jokes that match users’ tastes Build autoencoders for credit card fraud detection Work with image recognition and convolutional neural networks Make predictions for casino slot machines using reinforcement learning Implement natural language processing (NLP) techniques for sentiment analysis and customer segmentation Produce simple and effective data visualizations for improved insights Use NLP to extract insights for text Implement tree-based classifiers including random forest and boosted tree Who this book is for If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning techniques using R, this is an ideal Learning Path for you. …”

“…Use a Telco data set, to predict customer churn using Random Forests. In Detail Spark juggernaut keeps on rolling and getting more and more momentum each day. …”

“…What You Will Learn Master basic statistics, descriptive statistics, and probability theory Explore ML methods, including decision trees and decision forests Learn probability distributions normal and Poisson distributions Explore hypothesis testing, p-values, types I and II error handling Master logistic regression, linear regression, and regression trees Learn correlation, R-Square, RMSE, MAE, and coefficient of determination Audience This beginner-level course has been niched to cater to an individual looking to master statistics and probability for data science and analysis, an individual looking to pursue a career in data science, or professionals and students wanting to understand statistics for data analysis. …”

“…You will gradually develop key computational skills such as creating reusable workflows in R Markdown and packages for code reuse.By the end of this book, you'll have gained a solid understanding of the most important and widely used techniques in bioinformatic analysis and the tools you need to work with real biological data.What you will learnEmploy Bioconductor to determine differential expressions in RNAseq dataRun SAMtools and develop pipelines to find single nucleotide polymorphisms (SNPs) and IndelsUse ggplot to create and annotate a range of visualizationsQuery external databases with Ensembl to find functional genomics informationExecute large-scale multiple sequence alignment with DECIPHER to perform comparative genomicsUse d3.js and Plotly to create dynamic and interactive web graphicsUse k-nearest neighbors, support vector machines and random forests to find groups and classify dataWho this book is forThis book is for bioinformaticians, data analysts, researchers, and R developers who want to address intermediate-to-advanced biological and bioinformatics problems by learning through a recipe-based approach. …”