2018年好书 851页基于R语言Data Science and Predictive Analytics

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收藏 2020-05-07

Chap 1 (Motivation)
Chap 2 (Foundations of R)
Chap 3 (Managing Data in R)
Chap 4 (Visualization)
Chap 5 (Linear Algebra and Matrix Computing)
Chap 6 (Dimensionality Reduction)
Chap 7 (Lazy Learning – Classiﬁcation Using Nearest Neighbors)
Chap 8 (Probabilistic Learning: Classiﬁcation Using Naive Bayes)
Chap 9 (Decision Tree Divide and Conquer Classiﬁcation)
Chap 10 (Forecasting Numeric Data Using Regression Models)
Chap 11 (Black Box Machine-Learning Methods: Neural Networks and Support Vector Machines)
Chap 12 (Apriori Association Rules Learning)
Chap 13 (k-Means Clustering)
Chap 14 (Model Performance Assessment)
Chap 15 (Improving Model Performance)
Chap 16 (Specialized Machine Learning Topics)
Chap 17 (Variable/Feature Selection)
Chap 18 (Regularized Linear Modeling and Controlled Variable Selection)
Chap 19 (BigBig Longitudinal Data Analysis)
Chap 20 (Natural Language Processing/Text Mining)
Chap 21 (Prediction and Internal Statistical Cross Validation)
Chap 22 (Function Optimization)
Chap 23 (Deep Learning)

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