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دانلود کتاب Mastering Predictive Analytics with R
دانلود کتاب تسلط بر تجزیه و تحلیل پیش بینی کننده با R
مشخصات کتاب
Mastering Predictive Analytics with R
ویرایش: 2nd edition نویسندگان: Forte. Rui Miguel, Miller. James D سری: ISBN (شابک) : 9781787124356, 1787124355 ناشر: Packt Publishing سال نشر: 2017 تعداد صفحات: 613 زبان: English فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 7 مگابایت
قیمت کتاب (تومان) : 34,000
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توجه داشته باشید کتاب تسلط بر تجزیه و تحلیل پیش بینی کننده با R نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی درمورد کتاب به خارجی
Problems with linear regression -- Multicollinearity -- Outliers -- Feature selection -- Regularization -- Ridge regression -- Least absolute shrinkage and selection operator (lasso) -- Implementing regularization in R -- Polynomial regression -- Summary -- Chapter 4: Generalized Linear Models -- Classifying with linear regression -- Introduction to logistic regression -- Generalized linear models -- Interpreting coefficients in logistic regression -- Assumptions of logistic regression -- Maximum likelihood estimation -- Predicting heart disease -- Assessing logistic regression models -- Model deviance -- Test set performance -- Regularization with the lasso -- Classification metrics -- Extensions of the binary logistic classifier -- Multinomial logistic regression -- Predicting glass type -- Ordinal logistic regression -- Predicting wine quality -- Poisson regression -- Negative Binomial regression -- Summary -- Chapter 5: Neural Networks -- The biological neuron -- The artificial neuron -- Stochastic gradient descent -- Gradient descent and local minima -- The perceptron algorithm -- Linear separation -- The logistic neuron -- Multilayer perceptron networks -- Training multilayer perceptron networks -- The back propagation algorithm -- Predicting the energy efficiency of buildings -- Evaluating multilayer perceptrons for regression -- Predicting glass type revisited -- Predicting handwritten digits -- Receiver operating characteristic curves -- Radial basis function networks -- Summary -- Chapter 6: Support Vector Machines -- Maximal margin classification -- Support vector classification -- Inner products -- Kernels and support vector machines -- Predicting chemical biodegration -- Predicting credit scores -- Multiclass classification with support vector machines -- Summary -- Chapter 7: Tree-Based Methods -- The intuition for tree models;Algorithms for training decision trees -- Classification and regression trees -- CART regression trees -- Tree pruning -- Missing data -- Regression model trees -- CART classification trees -- C5.0 -- Predicting class membership on synthetic 2D data -- Predicting the authenticity of banknotes -- Predicting complex skill learning -- Tuning model parameters in CART trees -- Variable importance in tree models -- Regression model trees in action -- Improvements to the M5 model -- Summary -- Chapter 8: Dimensionality Reduction -- Defining DR -- Correlated data analyses -- Scatterplots -- Causation -- The degree of correlation -- Reporting on correlation -- Principal component analysis -- Using R to understand PCA -- Independent component analysis -- Defining independence -- ICA pre-processing -- Factor analysis -- Explore and confirm -- Using R for factor analysis -- The output -- NNMF -- Summary -- Chapter 9: Ensemble Methods -- Bagging -- Margins and out-of-bag observations -- Predicting complex skill learning with bagging -- Predicting heart disease with bagging -- Limitations of bagging -- Boosting -- AdaBoost -- AdaBoost for binary classification -- Predicting atmospheric gamma ray radiation -- Predicting complex skill learning with boosting -- Limitations of boosting -- The importance of variables in random forests -- XGBoost -- Summary -- Chapter 10: Probabilistic Graphical Models -- A little graph theory -- Bayes' theorem -- Conditional independence -- Bayesian networks -- The Naïve Bayes classifier -- Predicting the sentiment of movie reviews -- Predicting promoter gene sequences -- Predicting letter patterns in English words -- Summary -- Chapter 11 : Topic Modeling -- An overview of topic modeling -- Latent Dirichlet Allocation -- The Dirichlet distribution -- The generative process -- Fitting an LDA model;Modeling the topics of online news stories -- Model stability -- Finding the number of topics -- Topic distributions -- Word distributions -- LDA extensions -- Modeling tweet topics -- Word clouding -- Summary -- Chapter 12: Recommendation Systems -- Rating matrix -- Measuring user similarity -- Collaborative filtering -- User-based collaborative filtering -- Item-based collaborative filtering -- Singular value decomposition -- Predicting recommendations for movies and jokes -- Loading and pre-processing the data -- Exploring the data -- Evaluating binary top-N recommendations -- Evaluating non-binary top-N recommendations -- Evaluating individual predictions -- Other approaches to recommendation systems -- Summary -- Chapter 13: Scaling Up -- Starting the project -- Data definition -- Experience -- Data of scale - big data -- Using Excel to gauge your data -- Characteristics of big data -- Volume -- Varieties -- Sources and spans -- Structure -- Statistical noise -- Training models at scale -- Pain by phase -- Specific challenges -- Heterogeneity -- Scale -- Location -- Timeliness -- Privacy -- Collaborations -- Reproducibility -- A path forward -- Opportunities -- Bigger data, bigger hardware -- Breaking up -- Sampling -- Aggregation -- Dimensional reduction -- Alternatives -- Chunking -- Alternative language integrations -- Summary -- Chapter 14: Deep Learning -- Machine learning or deep learning -- What is deep learning? -- An alternative to manual instruction -- Growing importance -- Deeper data? -- Deep learning for IoT -- Use cases -- Word embedding -- Word prediction -- Word vectors -- Numerical representations of contextual similarities -- Netflix learns -- Implementations -- Deep learning architectures -- Artificial neural networks -- Recurrent neural networks -- Summary -- Index;Cover -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Gearing Up for Predictive Modeling -- Models -- Learning from data -- The core components of a model -- Our first model - k-nearest neighbors -- Types of model -- Supervised, unsupervised, semi-supervised, and reinforcement learning models -- Parametric and nonparametric models -- Regression and classification models -- Real-time and batch machine learning models -- The process of predictive modeling -- Defining the model's objective -- Collecting the data -- Picking a model -- Pre-processing the data -- Exploratory data analysis -- Feature transformations -- Encoding categorical features -- Missing data -- Outliers -- Removing problematic features -- Feature engineering and dimensionality reduction -- Training and assessing the model -- Repeating with different models and final model selection -- Deploying the model -- Summary -- Chapter 2: Tidying Data and Measuring Performance -- Getting started -- Tidying data -- Categorizing data quality -- The first step -- The next step -- The final step -- Performance metrics -- Assessing regression models -- Assessing classification models -- Assessing binary classification models -- Cross-validation -- Learning curves -- Plot and ping -- Summary -- Chapter 3: Linear Regression -- Introduction to linear regression -- Assumptions of linear regression -- Simple linear regression -- Estimating the regression coefficients -- Multiple linear regression -- Predicting CPU performance -- Predicting the price of used cars -- Assessing linear regression models -- Residual analysis -- Significance tests for linear regression -- Performance metrics for linear regression -- Comparing different regression models -- Test set performance
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