“…Languages, Compilers and Tools for Embedded Systems '08 : proceedings of the 2008 Association for Computing Machinery Special Interest Group on Programming Languages-Special Interest Group on Embedded Systems Conference on Languages, Compilers and Tools for Embedded Systems : Tucson, Arizona, United States of America, June 12-13, 2008…”

“…LCTES '04 Languages, Compilers, and Tools for Embedded Systems 2004, Washington - June 11 - 13, 2004…”

“…Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. …”

Tabla de Contenidos: “…AWS Penetration Testing: Beginner's guide to hacking AWS with tools such as Kali Linux, Metasploit, and Nmap…”

Tabla de Contenidos: “…Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Part I - Data Ingestion -- Chapter 1: Tabular Formats -- Tidying Up -- CSV -- Sanity Checks -- The Good, the Bad, and the Textual Data -- The Bad -- The Good -- Spreadsheets Considered Harmful -- SQL RDBMS -- Massaging Data Types -- Repeating in R -- Where SQL Goes Wrong (and How to Notice It) -- Other Formats -- HDF5 and NetCDF-4 -- Tools and Libraries -- SQLite -- Apache Parquet -- Data Frames -- Spark/Scala -- Pandas and Derived Wrappers -- Vaex -- Data Frames in R (Tidyverse) -- Data Frames in R (data.table) -- Bash for Fun -- Exercises -- Tidy Data from Excel -- Tidy Data from SQL -- Denouement -- Chapter 2: Hierarchical Formats -- JSON -- What JSON Looks Like -- NaN Handling and Data Types -- JSON Lines -- GeoJSON -- Tidy Geography -- JSON Schema -- XML -- User Records -- Keyhole Markup Language -- Configuration Files -- INI and Flat Custom Formats -- TOML -- Yet Another Markup Language -- NoSQL Databases -- Document-Oriented Databases -- Missing Fields -- Denormalization and Its Discontents -- Key/Value Stores -- Exercises -- Exploring Filled Area -- Create a Relational Model -- Denouement -- Chapter 3: Repurposing Data Sources -- Web Scraping -- HTML Tables -- Non-Tabular Data -- Command-Line Scraping -- Portable Document Format -- Image Formats -- Pixel Statistics -- Channel Manipulation -- Metadata -- Binary Serialized Data Structures -- Custom Text Formats -- A Structured Log -- Character Encodings -- Exercises -- Enhancing the NPY Parser -- Scraping Web Traffic -- Denouement -- Part II - The Vicissitudes of Error -- Chapter 4: Anomaly Detection -- Missing Data -- SQL -- Hierarchical Formats -- Sentinels -- Miscoded Data -- Fixed Bounds -- Outliers -- Z-Score -- Interquartile Range -- Multivariate Outliers -- Exercises -- A Famous Experiment -- Misspelled Words…”

“…Learn to effectively manage data and execute data science projects from start to finish using PythonKey FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook DescriptionPractical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. …”

“…Later chapters will draw focus to the wide range of tools that help in forensics investigations and incident response mechanisms. …”

Tabla de Contenidos: “…Controlling memory size -- Heterogeneous lists -- From lists to multidimensional arrays -- Resizing arrays -- Arrays derived from NumPy functions -- Getting an array directly from a file -- Extracting data from pandas -- NumPy fast operation and computations -- Matrix operations -- Slicing and indexing with NumPy arrays -- Stacking NumPy arrays -- Working with sparse arrays -- Summary -- Chapter 3: The Data Pipeline -- Introducing EDA -- Building new features -- Dimensionality reduction -- The covariance matrix -- Principal component analysis -- PCA for big data - RandomizedPCA -- Latent factor analysis -- Linear discriminant analysis -- Latent semantical analysis -- Independent component analysis -- Kernel PCA -- T-SNE -- Restricted Boltzmann Machine -- The detection and treatment of outliers -- Univariate outlier detection -- EllipticEnvelope -- OneClassSVM -- Validation metrics -- Multilabel classification -- Binary classification -- Regression -- Testing and validating -- Cross-validation -- Using cross-validation iterators -- Sampling and bootstrapping -- Hyperparameter optimization -- Building custom scoring functions -- Reducing the grid search runtime -- Feature selection -- Selection based on feature variance -- Univariate selection -- Recursive elimination -- Stability and L1-based selection -- Wrapping everything in a pipeline -- Combining features together and chaining transformations -- Building custom transformation functions -- Summary -- Chapter 4: Machine Learning -- Preparing tools and datasets -- Linear and logistic regression -- Naive Bayes -- K-Nearest Neighbors -- Nonlinear algorithms -- SVM for classification -- SVM for regression -- Tuning SVM -- Ensemble strategies -- Pasting by random samples -- Bagging with weak classifiers -- Random Subspaces and Random Patches -- Random Forests and Extra-Trees…”

“…Powerful tools for analyzing the numbers and making the best decisions for your business…”

“…This book is a comprehensive guide that covers every facet of developing, debugging, and publishing extensions that amplify your productivity, tooling, and analysis within the Visual Studio IDE. …”