Tabla de Contenidos: “…-- Comparing Selection Criteria -- Forced Inclusion of Model Effects -- Example: Predicting the Close Rate of Retail Stores -- Example: Building a Model with Forward Selection -- Example: Building a Model with the Lasso and SBC -- Example: Building a Model with the Lasso and Cross Validation -- Example: Building a Model with the Lasso and Validation Data…”

Tabla de Contenidos: “…-- Lesson F: Layers on the Move -- Drag, Keep Dragging, and Drop -- A Few Selection Methods -- Lesson G: Intro to Smart Objects and Filters -- Nondestructive Alteration -- A Note Regarding Navigation -- 2 A Few Adjustments -- Lesson A: The Color Wheel -- Additive Primaries -- Complementary Colors -- Fixing Only What's Wrong -- Lesson B: Photo Filter Adjustment -- Adjustment as Diagnostic and Cure -- Lesson C: Curves -- Tone Plus Color -- Monochromatic Contrast Enhancement -- Per-Channel Contrast Enhancement -- Auto and Semi-Auto Curves -- Lesson D: Vibrance &amp -- Hue/Saturation -- Definition of Saturation -- Color Clipping -- Vibrance -- Hue/Saturation -- Limiting Adjustments -- 3 Selections &amp -- Masking -- Warm-Up -- Lesson A: Rectangular Marquee -- Basic Use -- Modifier Keys -- Lesson B: Elliptical Marquee -- Vibrance Adjustment -- Lesson C: Lasso &amp -- Polygonal Lasso -- The Lasso Tool -- The Polygonal Lasso Tool -- Lesson D: Object Selection -- Object Selection Tool…”

“…Using JMP 13 and JMP 13 Pro, this book offers the following new and enhanced features in an example-driven format: an add-in for Microsoft Excel Graph Builder dirty data visualization regression ANOVA logistic regression principal component analysis LASSO elastic net cluster analysis decision trees k -nearest neighbors neural networks bootstrap forests boosted trees text mining association rules model comparison With today’s emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis. …”

Tabla de Contenidos: “…""Creating vectors""""Creating a labeled point""; ""Creating matrices""; ""Calculating summary statistics""; ""Calculating correlation""; ""Doing hypothesis testing""; ""Creating machine learning pipelines using ML""; ""Chapter 7: Supervised Learning with MLlib Regression""; ""Introduction""; ""Using linear regression""; ""Understanding cost function""; ""Doing linear regression with lasso""; ""Doing ridge regression""; ""Chapter 8: Supervised Learning with MLlib â€? …”

“…In this video, you will learn regression techniques in Python using ordinary least squares, ridge, lasso, decision trees, and neural networks. We start by exploring a census dataset that captures sales from a business in various counties across the United States. …”

Tabla de Contenidos: “…Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Introduction -- Audience -- Course Adoption -- Errors -- Acknowledgments -- About the Authors -- Chapter 1: Data Collection and Cleaning -- Data-Collection Strategies -- Data Preprocessing Strategies -- Programming with R -- Data Types in R -- Data Structures in R -- Package Installation in R -- Reading and Writing Data in R -- Using the FOR Loop in R -- Using the WHILE Loop in R -- Using the IF-ELSE Statement in R -- Programming with Python -- Data Wrangling and Analytics in R and Python -- Structuring and Cleaning Data -- Missing Data -- Strategies for Dealing with Missing Data -- Data Deduplication -- Summary -- Exercise -- Notes -- References -- Chapter 2: Mathematical Background for Predictive Analytics -- Basics of Linear Algebra -- Vectors and Matrices -- Determinant -- Simple Linear Regression (SLR) -- Principal Component Analysis (PCA) -- Singular Value Decomposition (SVD) -- Introduction to Neural Networks -- Summary -- Exercise -- References -- Chapter 3: Introduction to Statistics, Probability, and Information Theory for Analytics -- Normal Distribution and the Central Limit Theorem -- Pearson Correlation Coefficient and Covariance -- Basic Probability for Predictive Analytics -- Conditional Probability -- Bayes' Theorem and Bayesian Classifiers -- Information Theory for Predictive Modeling -- Summary -- Exercise -- Notes -- References -- Chapter 4: Introduction to Machine Learning -- Statistical versus Machine Learning Models -- Regression Techniques -- Multiple Linear Regression (MLR) Model -- Assumptions of MLR -- Introduction to Multinomial Logistic Regression (MLogR) -- Bias versus Variance Trade-off -- Overfitting and Underfitting -- Regularization -- Ridge Regression -- Lasso Regression -- Summary -- Exercise…”

Tabla de Contenidos: “…Classifications -- Components -- Credits -- Dimensions -- File -- Geo-location -- Rendering -- Statistics -- Text -- Units -- Preferences -- Accessibility -- Applications -- Compatibility -- Drawing -- Files -- General -- Graphics (Formerly OpenGL) -- Shortcuts -- Template -- Workspace -- Summary -- Chapter 11: Working with Components -- Using the Components Panel -- Creating Components -- Creating New Components -- Nested Components -- Component Statistics -- Updating and Editing Components -- Component Instances -- Instance Options -- Dynamic and Live Components -- Dynamic Components -- Live Components -- Summary -- Chapter 12: Import, Export, 3D Warehouse, and Extensions -- Import Options -- Importing 3D Files -- 2D Files -- Image Files -- Export Options -- Exporting 3D Files -- Exporting 2D Files -- Image Files -- Section Slices -- Animations -- Send to LayOut -- 3D Warehouse -- Extensions -- Extension Warehouse -- Extension Manager -- Overlays -- Summary -- Index -- Other Books You May Enjoy…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright and Credits -- Dedication -- Packt Upsell -- Contributors -- Table of Contents -- Preface -- Chapter 1: A Gentle Introduction to Machine Learning -- Introduction - classic and adaptive machines -- Descriptive analysis -- Predictive analysis -- Only learning matters -- Supervised learning -- Unsupervised learning -- Semi-supervised learning -- Reinforcement learning -- Computational neuroscience -- Beyond machine learning - deep learning and bio-inspired adaptive systems -- Machine learning and big data -- Summary -- Chapter 2: Important Elements in Machine Learning -- Data formats -- Multiclass strategies -- One-vs-all -- One-vs-one -- Learnability -- Underfitting and overfitting -- Error measures and cost functions -- PAC learning -- Introduction to statistical learning concepts -- MAP learning -- Maximum likelihood learning -- Class balancing -- Resampling with replacement -- SMOTE resampling -- Elements of information theory -- Entropy -- Cross-entropy and mutual information -- Divergence measures between two probability distributions -- Summary -- Chapter 3: Feature Selection and Feature Engineering -- scikit-learn toy datasets -- Creating training and test sets -- Managing categorical data -- Managing missing features -- Data scaling and normalization -- Whitening -- Feature selection and filtering -- Principal Component Analysis -- Non-Negative Matrix Factorization -- Sparse PCA -- Kernel PCA -- Independent Component Analysis -- Atom extraction and dictionary learning -- Visualizing high-dimensional datasets using t-SNE -- Summary -- Chapter 4: Regression Algorithms -- Linear models for regression -- A bidimensional example -- Linear regression with scikit-learn and higher dimensionality -- R2 score -- Explained variance -- Regressor analytic expression -- Ridge, Lasso, and ElasticNet -- Ridge -- Lasso…”

Tabla de Contenidos: “…Cover -- Title Page -- Copyright -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1: Concepts of Machine Learning -- Chapter 1: Introduction to Machine Learning with Qlik -- Introduction to Qlik tools -- Insight Advisor -- Qlik AutoML -- Advanced Analytics Integration -- Basic statistical concepts with Qlik solutions -- Types of data -- Mean, median, and mode -- Variance -- Standard deviation -- Standardization -- Correlation -- Probability -- Defining a proper sample size and population -- Defining a sample size -- Training and test data in machine learning -- Concepts to analyze model performance and reliability -- Regression model scoring -- Multiclass classification scoring and binary classification scoring -- Feature importance -- Summary -- Chapter 2: Machine Learning Algorithms and Models with Qlik -- Regression models -- Linear regression -- Logistic regression -- Lasso regression -- Clustering algorithms, decision trees, and random forests -- K-means clustering -- ID3 decision tree -- Boosting algorithms and Naive Bayes -- XGBoost -- Gaussian Naive Bayes -- Neural networks, deep learning, and natural-language models -- Summary -- Chapter 3: Data Literacy in a Machine Learning Context -- What is data literacy? …”

“…Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. …”

“…Familiarity with mathematical concepts such as algebra and basic statistics will also be useful…”

Tabla de Contenidos: “…. -- See also -- Linear regression API with Lasso and L-BFGS in Spark 2.0 -- How to do it... -- How it works... -- There's more... -- See also -- Linear regression API with Lasso and 'auto' optimization selection in Spark 2.0 -- How to do it... -- How it works... -- There's more…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: A Gentle Introduction to Machine Learning -- Introduction - classic and adaptive machines -- Only learning matters -- Supervised learning -- Unsupervised learning -- Reinforcement learning -- Beyond machine learning - deep learning and bio-inspired adaptive systems -- Machine learning and big data -- Further reading -- Summary -- Chapter 2: Important Elements in Machine Learning -- Data formats -- Multiclass strategies -- One-vs-all -- One-vs-one -- Learnability -- Underfitting and overfitting -- Error measures -- PAC learning -- Statistical learning approaches -- MAP learning -- Maximum-likelihood learning -- Elements of information theory -- References -- Summary -- Chapter 3: Feature Selection and Feature Engineering -- scikit-learn toy datasets -- Creating training and test sets -- Managing categorical data -- Managing missing features -- Data scaling and normalization -- Feature selection and filtering -- Principal component analysis -- Non-negative matrix factorization -- Sparse PCA -- Kernel PCA -- Atom extraction and dictionary learning -- References -- Summary -- Chapter 4: Linear Regression -- Linear models -- A bidimensional example -- Linear regression with scikit-learn and higher dimensionality -- Regressor analytic expression -- Ridge, Lasso, and ElasticNet -- Robust regression with random sample consensus -- Polynomial regression -- Isotonic regression -- References -- Summary -- Chapter 5: Logistic Regression -- Linear classification -- Logistic regression -- Implementation and optimizations -- Stochastic gradient descent algorithms -- Finding the optimal hyperparameters through grid search -- Classification metrics -- ROC curve -- Summary -- Chapter 6: Naive Bayes…”

“…For instance, regression techniques - including logistic and Lasso - are presented as a single method, without using advanced linear algebra. …”

Tabla de Contenidos: “…17.5 Smoothing Signals -- 17.6 Logarithms and Other Transformations -- 17.7 Trends and Periodicity -- 17.8 Windowing -- 17.9 Brainstorming Simple Features -- 17.10 Better Features: Time Series as Vectors -- 17.11 Fourier Analysis: Sometimes a Magic Bullet -- 17.12 Time Series in Context: The Whole Suite of Features -- 17.13 Further Reading -- 17.14 Glossary -- Chapter 18 Probability -- 18.1 Flipping Coins: Bernoulli Random Variables -- 18.2 Throwing Darts: Uniform Random Variables -- 18.3 The Uniform Distribution and Pseudorandom Numbers -- 18.4 Nondiscrete, Noncontinuous Random Variables -- 18.5 Notation, Expectations, and Standard Deviation -- 18.6 Dependence, Marginal and Conditional Probability -- 18.7 Understanding the Tails -- 18.8 Binomial Distribution -- 18.9 Poisson Distribution -- 18.10 Normal Distribution -- 18.11 Multivariate Gaussian -- 18.12 Exponential Distribution -- 18.13 Log-Normal Distribution -- 18.14 Entropy -- 18.15 Further Reading -- 18.16 Glossary -- Chapter 19 Statistics -- 19.1 Statistics in Perspective -- 19.2 Bayesian versus Frequentist: Practical Tradeoffs and Differing Philosophies -- 19.3 Hypothesis Testing: Key Idea and Example -- 19.4 Multiple Hypothesis Testing -- 19.5 Parameter Estimation -- 19.6 Hypothesis Testing: t-Test -- 19.7 Confidence Intervals -- 19.8 Bayesian Statistics -- 19.9 Naive Bayesian Statistics -- 19.10 Bayesian Networks -- 19.11 Choosing Priors: Maximum Entropy or Domain Knowledge -- 19.12 Further Reading -- 19.13 Glossary -- Chapter 20 Programming Language Concepts -- 20.1 Programming Paradigms -- 20.2 Compilation and Interpretation -- 20.3 Type Systems -- 20.4 Further Reading -- 20.5 Glossary -- Chapter 21 Performance and Computer Memory -- 21.1 Example Script -- 21.2 Algorithm Performance and Big-O Notation -- 21.3 Some Classic Problems: Sorting a List and Binary Search…”

Tabla de Contenidos: “…A in Japan's Financial Sector -- 5.3 Methodology -- 5.3.1 Obtaining Excess Returns -- 5.3.2 Shareholder Value Creation Analysis -- 5.3.3 Performance Ratio Analysis -- 5.4 Data Description -- 5.5 Results -- 5.5.1 Shareholder Value Creation -- Cumulative abnormal returns-acquirer vs. target -- Cumulative abnormal returns-mega-mergers -- Cumulative abnormal returns-regression analysis -- 5.5.2 Performance Ratios -- Performance ratios-pre-merger acquirer vs. all banks -- Performance ratios-regression analysis -- 5.6 Conclusions -- Appendix -- Acknowledgments -- References -- 6 A Regime-Switching Analysis of Asian Bank Stocks -- 6.1 Introduction -- 6.2 RavenPack News Database -- 6.3 Data and Sample -- 6.3.1 Return Series -- 6.3.2 News Variables -- 6.4 Markov Regime-Switching (MRS) Model -- 6.5 Empirical Results -- 6.5.1 Descriptive Statistics of the Dataset -- 6.5.2 MRS-t Model Without News Variables -- 6.5.3 Effects of News Sentiment Sign -- 6.5.4 Effects of News Sentiment Dummy -- 6.5.5 Effects of News Sentiment -- 6.6 Conclusion -- 6.7 Appendix A: Selected 20 Asian Banks List -- 6.8 Appendix B: RavenPack Algorithms -- 6.8.1 Market Response Methodology -- 6.8.2 Expert Consensus Tagging Methodology -- 6.8.3 Factors in the Event Sentiment Score -- References -- 7 Embedded Predictor Selection for Default Risk Calculation: A Southeast Asian Industry Study -- 7.1 Introduction -- 7.2 Data and Default Predictors -- 7.3 Embedded Predictor Selection -- 7.3.1 Lasso and Elastic-Net Penalties -- 7.3.2 Regularization on Logit Model -- 7.4 Empirical Result -- 7.5 Conclusion -- Acknowledgment -- References -- 8 Demand for International Reserve and Monetary Disequilibrium: Evidence from Emerging Asia -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Stylized Facts and Adequacy of Reserves -- 8.3.1 Stylized Facts -- 8.3.2 Adequacy of Reserves…”

Tabla de Contenidos: “…Existing Continuous User Authentication Techniques -- 2.4. Statistical Language Modeling -- 2.4.1. Neural Networks -- 2.4.2. …”