Python机器学习经典实例(影印版)
作 者:(美)克里斯·阿尔本(Chris Albon) 著
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定 价:96
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出 版 社:东南大学出版社
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出版日期:2018年11月01日
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页 数:349
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装 帧:平装
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ISBN:9787564179786
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机器学习技术能够解决计算机安全问题，并最终为攻防双方之间的猫鼠游戏画上一个句号吗？或者说这只是炒作？现在你可以深入这一学科，自己回答这个问题了！有了这本实用指南，你就可以探索如何将机器学习应用于各种安全问题（如入侵检测、恶意软件分类和网络分析）。机器学习和安全专家Clarence Chio与David Freeman为讨论这两个领域之间的联姻提供了框架，另外还包括一个机器学习算法工具箱，你可以将其应用于一系列安全问题。本书适合于安全工程师和数据科学家。
![作者简介]()
克里斯·阿尔本是肯尼亚创业公司BRCK的首席数据科学家。
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Preface
1.Vectors,Matrices,and Arrays
1.0 Introduction
1.1 Creating a Vector
1.2 Creating a Matrix
1.3 Creating a Sparse Matrix
1.4 Selecting Elements
1.5 Describing a Matrix
1.6 Applying Operations to Elements
1.7 Finding the Maximum and Minimum Values
1.8 Calculating the Average,Variance,and Standard Deviation
1.9 Reshaping Arrays
1.10 Transposing a Vector or Matrix
1.11 Flattening a Matrix
1.12 Finding the Rank of a Matrix
1.13 Calculating the Determinant
1.14 Getting the Diagonal of a Matrix
1.15 Calculating the Trace of a Matrix
1.16 Finding Eigenvalues and Eigenvectors
1.17 Calculating Dot Products
1.18 Adding and Subtracting Matrices
1.19 Multiplying Matrices
1.20 Inverting a Matrix
1.21 Generating Random Values
2.Loading Data
2.0 Introduction
2.1 Loading a Sample Dataset
2.2 Creating a Simulated Dataset
2.3 Loading a CSV File
2.4 Loading an Excel File
2.5 Loading a ]SON File
2.6 Querying a SQL Database
3.Data Wrangling
3.0 Introduction
3.1 Creating a Data Frame
3.2 Describing the Data
3.3 Navigating DataFrames
3.4 Selecting Rows Based on Conditionals
3.5 Replacing Values
3.6 Renaming Columns
3.7 Finding the Minimum,Maximum,Sum,Average,and Count
3.8 Finding Unique Values
3.9 Handling Missing Values
3.10 Deleting a Column
3.11 Deleting a Row
3.12 Dropping Duplicate Rows
3.13 Grouping Rows by Values
3.14 Grouping Rows by Time
3.15 Looping Over a Column
3.16 Applying a Function Over All Elements in a Column
3.17 Applying a Function to Groups
3.18 Concatenating DataFrames
3.19 Merging DataFrames
4.Handling Numerical Data
4.0 Introduction
4.1 Rescaling a Feature
4.2 Standardizing a Feature
4.3 Normalizing Observations
4.4 Generating Polynomial and Interaction Features
4.5 Transforming Features
4.6 Detecting Outliers
4.7 Handling Outliers
4.8 Discretizating Features
4.9 Grouping Observations Using Clustering
4.10 Deleting Observations with Missing Values
4.11 Imputing Missing Values
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5.Handling Categorical Data
6.Handling Text
7.Handling Dates and Times
8.Handling Images
9.Dimensionality Reduction Using Feature Extraction
10.Dimensionality Reduction Using Feature Selection
11.Model Evaluation
12.Model Selection
13.Linear Regression
14.Trees and Forests
15.K-Nearest Neighbors
16.Logistic Regression
17.Support Vector Machines
18.Naive Bayes
19.Clustering
20.Neural Networks
21.Saving and Loading Trained Models
Index
