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Machine learning for email /
Table of Contents: “…-- Inferring the Types of Columns in Your Data -- Inferring Meaning -- Numeric Summaries -- Means, Medians, and Modes -- Quantiles -- Standard Deviations and Variances -- Exploratory Data Visualization -- Modes -- Skewness -- Thin Tails vs. …”
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2
Oracle ADF enterprise application development-made simple : successfully plan, develop, test, and deploy enterpise applications with Oracle ADF /
Table of Contents: “…Deciding how to build itDeciding how much to build at a time; Deciding how much to build yourself; Deciding how to integrate; Application architecture; Example Work Breakdown Structure; Estimating the solution; Top-down estimate; Bottom-up estimate; Three-point estimates; Grouping -- simple, normal, and hard; More input, better estimates; Adding it all up -- the final estimate; Swings and roundabouts; Calculating the standard deviation for a task; Calculating the standard deviation for a project; Sanity check; From effort to calendar time; Summary; Chapter 3:Getting Organized.…”
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3
Machine learning for hackers /
Table of Contents: “…; Inferring the Types of Columns in Your Data; Inferring Meaning; Numeric Summaries; Means, Medians, and Modes; Quantiles; Standard Deviations and Variances; Exploratory Data Visualization; Visualizing the Relationships Between Columns; Chapter 3. …”
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4
Engaging the avatar : new frontiers in immersive education /
Table of Contents: “…""Student recommendations for environmental features""""student recommendations for window/ lighting features""; ""student recommendations for floor design""; ""student recommendations for wall design""; ""student recommendations for roof/ceiling design""; ""student recommendations for seating arrangements""; ""student recommendations for space design""; ""student recommendations for entrance design""; ""student recommendations for space circulation design""; ""total number of votes and standard deviation percentage between student categories per design element""; ""future research""…”
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5
Unlocking financial data : a practical guide to technology for equity and fixed income analysts /
Table of Contents: “…Portfolio Risk Analysis""; ""Path 1: Excel""; ""Variance, Volatility, and Standard Deviation""; ""Sharpe Ratio with Historical or Forecasted Returns""; ""Portfolio Breakdown""; ""Warning Signs""; ""Path 2: Access""; ""Portfolio Breakdown""; ""Warning Signs""; ""Path 3: C#""…”
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Unlocking financial data : a practical guide to technology for equity and fixed income analysts /
Table of Contents: “…Portfolio Risk Analysis""; ""Path 1: Excel""; ""Variance, Volatility, and Standard Deviation""; ""Sharpe Ratio with Historical or Forecasted Returns""; ""Portfolio Breakdown""; ""Warning Signs""; ""Path 2: Access""; ""Portfolio Breakdown""; ""Warning Signs""; ""Path 3: C#""…”
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7
F♯ for quantitative finance : an introductory guide to utilizing F♯ for quantitative finance leveraging the .NET platform /
Table of Contents: “…Floating-point numbersThe IEEE 754 floating-point standard; Learning about numerical types in F♯; Arithmetic operators; Learning about arithmetic comparisons; Math operators; Conversion functions; Introducing statistics; Aggregate statistics; Calculating the sum of a sequence; Calculating the average of a sequence; Calculating the minimum of a sequence; Calculating the maximum of a sequence; Calculating the variance and standard deviation of a sequence; Looking at an example application; Using the Math.NET library; Installing the Math.NET library; Introduction to random number generation.…”
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8
Principles of programming & coding /
Table of Contents: “…3D printing -- Algorithms -- American Standard Code for Information -- Interchange (ASCII) -- Android OS -- Application -- Autonomic computing -- Avatars and simulation -- Binary hexadecimal representations -- Boolean operators -- Branching logic -- Characters and strings -- Cloud computing -- Coding and encryption -- Color coding -- Combinatorics -- Comment programming -- Comparison operators -- Computer animation -- Computer memory -- Computer modeling -- Computer security -- Computer-aided design (CAD) -- Computer-aided design and computer-aided manufacturing software (CAD/CAM) -- Computer-assisted instruction (CAI) -- Conditional operators -- Constraint programming -- Control systems -- Cowboy coding -- CPU design -- Crowdfunding -- Crowdsourcing -- Cryptography -- Data mining -- Data warehouse -- Database design -- Database structuring conventions -- Debugging -- Device drivers -- Diffusion of innovations -- Digital divide -- Digital forensics -- Digital libraries -- Digital native -- Digital photography -- Digital signal processors (DSP) -- Digital watermarking -- Disk operating system (DOS) -- Drone warfare -- Drones -- E-banking -- E-learning -- Electronic circuits -- Electronic communication software -- Encryption -- Error handling -- Event-driven marketing (EDM) -- Expectancy theory -- Experimenter's bias -- Extreme programming -- Firewalls -- Firmware -- Functional electrical stimulation (FES) -- Game programming -- Gamification -- Graphical user interface (GUI) -- Graphics formats -- Guard clause -- HTTP cookie -- Imagined communities -- Incremental development -- Informational technology (IT) -- Information visualization -- Internet Protocol (IP) -- Inversion of control (Hollywood Principle) -- iOS -- Iterative constructs -- Java programming language -- JavaScript -- Knowledge worker -- Levels of processing theory -- Logic synthesis -- Logistics -- Machine learning -- Malware -- Massive open online course (MOOC) -- Meta-analysis -- Metacomputing -- Metadata -- Microprocessors -- Mixed methods research (MMR) -- Mobile apps -- Mobile technology -- Motherboards -- Multiprocessing operating systems (OS) -- Multi-user operating system (OS) -- Naming conventions -- Net neutrality -- Network security -- Neuro-linguistic programming (NLP) -- Neuromarketing -- Neuromorphic chips -- Objectivity -- Object-oriented design (OOD) -- Object-oriented programming (OOP) -- Privacy rights -- Programming languages -- Prototyping -- Quantum computing -- Random access memory (RAM) -- Rapid application development (RAD) -- Rational choice theory -- Search engine optimization (SEO) -- Semantic memory -- Semantics -- Signal processing -- Source code comments -- Spiral development -- Standard deviation -- Standpoint theory -- Statistical inference -- String-oriented symbolic languages (SNOBOL) -- Structural equation modeling (SEM) -- Technology in education -- Test doubles -- Theory of multiple intelligences -- Theory X and Theory Y -- Transformation priority premise (TPP) -- Tree structures -- Turing test -- Uncertainty reduction theory (URT) -- Unicode -- UNIX -- Variables and values -- Waterfall development -- Web design -- Web graphic design -- Working memory -- Worse-is-better -- Time Line of Inventions and Advancements in Programming and Coding.…”
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9
Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications /
Table of Contents: “…Measuring spread -- quartiles and the five-number summaryVisualizing numeric variables -- boxplots; Visualizing numeric variables -- histograms; Understanding numeric data -- uniform and normal distributions; Measuring spread -- variance and standard deviation; Exploring categorical variables; Measuring the central tendency -- the mode; Exploring relationships between variables; Visualizing relationships -- scatterplots; Examining relationships -- two-way cross-tabulations; Summary; Chapter 3: Lazy Learning -- Classification using Nearest Neighbors; Understanding classification using nearest neighbors.…”
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10
Learning pandas /
Table of Contents: “…Categorical values as an index -- CategoricalIndex -- Indexing by date and time using DatetimeIndex -- Indexing periods of time using PeriodIndex -- Working with Indexes -- Creating and using an index with a Series or DataFrame -- Selecting values using an index -- Moving data to and from the index -- Reindexing a pandas object -- Hierarchical indexing -- Summary -- Chapter 7: Categorical Data -- Configuring pandas -- Creating Categoricals -- Renaming categories -- Appending new categories -- Removing categories -- Removing unused categories -- Setting categories -- Descriptive information of a Categorical -- Munging school grades -- Summary -- Chapter 8: Numerical and Statistical Methods -- Configuring pandas -- Performing numerical methods on pandas objects -- Performing arithmetic on a DataFrame or Series -- Getting the counts of values -- Determining unique values (and their counts) -- Finding minimum and maximum values -- Locating the n-smallest and n-largest values -- Calculating accumulated values -- Performing statistical processes on pandas objects -- Retrieving summary descriptive statistics -- Measuring central tendency: mean, median, and mode -- Calculating the mean -- Finding the median -- Determining the mode -- Calculating variance and standard deviation -- Measuring variance -- Finding the standard deviation -- Determining covariance and correlation -- Calculating covariance -- Determining correlation -- Performing discretization and quantiling of data -- Calculating the rank of values -- Calculating the percent change at each sample of a series -- Performing moving-window operations -- Executing random sampling of data -- Summary -- Chapter 9: Accessing Data -- Configuring pandas -- Working with CSV and text/tabular format data -- Examining the sample CSV data set -- Reading a CSV file into a DataFrame.…”
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Microsoft Azure machine learning : explore predictive analytics using step-by-step tutorials and build models to make prediction in a jiffy with a few mouse clicks /
Table of Contents: “…Cover -- Copyright -- Credits -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: Introduction -- Introduction to predictive analytics -- Problem definition and scoping -- Data collection -- Data exploration and preparation -- Model development -- Model deployment -- Machine learning -- Kinds of machine learning problems -- Classification -- Regression -- Clustering -- Common machine learning techniques/algorithms -- Linear regression -- Logistic regression -- Decision tree-based ensemble models -- Neural networks and deep learning -- Introduction to Azure Machine Learning -- ML Studio -- Summary -- Chapter 2: ML Studio Inside Out -- Introduction to ML Studio -- Getting started with Microsoft Azure -- Microsoft account and subscription -- Creating and managing ML workspaces -- Inside ML Studio -- Experiments -- Creating and editing an experiment -- Running an experiment -- Creating and running an experiment -- do it yourself -- Workspace as a collaborative environment -- Summary -- Chapter 3: Data Exploration and Visualization -- The basic concepts -- The mean -- The median -- Standard deviation and variance -- Understanding a histogram -- The box and whiskers plot -- The outliers -- A scatter plot -- Data exploration in ML Studio -- Visualizing an automobile price dataset -- A histogram -- The box and whiskers plot -- Comparing features -- A snapshot -- Do it yourself -- Summary -- Chapter 4: Getting Data in and out of ML Studio -- Getting data in ML Studio -- Uploading data from a PC -- The Enter Data module -- The Data Reader module -- Getting data from the Web -- Getting data from Azure -- Data format conversion -- Getting data from ML Studio -- Saving dataset in a PC -- Saving results in ML Studio -- The Writer module -- Summary -- Chapter 5: Data Preparation.…”
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