Python機器學習經典實例(2018年東南大學出版社出版的圖書)

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《Python機器學習經典實例》是2018年東南大學出版社出版的圖書,作者是ChrisAlbon。

基本介紹

  • 中文名:Python機器學習經典實例
  • 作者:ChrisAlbon
  • 出版時間:2018年
  • 出版社:東南大學出版社
  • ISBN:9787564179786
內容簡介,圖書目錄,作者簡介,

內容簡介

  《Python機器學習經典實例(影印版 英文版)》這本實用指南提供了近200則完整的攻略,可幫助你解決日常工作中可能遇到的機器學習難題。如果你熟悉Python以及包括pandas和scikit-learn在內的庫,那么解決一些特定問題將不在話下,比如數據載入、文本處理、數值數據、模型選擇、降維以及諸多其他主題。
  每則攻略中都包含代碼,你可以將其複製並貼上到實驗數據集中,以確保代碼的確有效。你可以插入、組合、修改這些代碼,從而協助構建你自己的應用程式。攻略中還包括相關的討論,對解決方案給出了解釋並提供有意義的上下文。
  《Python機器學習經典實例(影印版 英文版)》在理論和概念之外提供了構造實用機器學習套用所需的具體細節。

圖書目錄

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
……
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

作者簡介

  克里斯·阿爾本是肯亞創業公司BRCK的首席數據科學家。

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