دانلود کتاب Data Cleaning and Exploration with Machine Learning: Get to grips with machine learning techniques to achieve sparkling-clean data quickly

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Cover
Title page
Copyright and Credits,
Contributors
Table of Contents
Preface
Section 1 – Data Cleaning and Machine Learning Algorithms
Chapter 1: Examining the Distribution of Features and Targets
Technical requirements
Subsetting data
Generating frequencies for categorical features
Generating summary statistics for continuous and discrete features
Identifying extreme values and outliers in univariate analysis
Using histograms, boxplots, and violin plots to examine the distribution of features
Using histograms
Using boxplots
Using violin plots
Summary
Chapter 2: Examining Bivariate and Multivariate Relationships between Features and Targets
Technical requirements
Identifying outliers and extreme values in bivariate relationships
Using scatter plots to view bivariate relationships between continuous features
Using grouped boxplots to view bivariate relationships between continuous and categorical features
Using linear regression to identify data points with significant influence
Using K-nearest neighbors to find outliers
Using Isolation Forest to find outliers
Summary
Chapter 3: Identifying and Fixing Missing Values
Technical requirements
Identifying missing values
Cleaning missing values
Imputing values with regression
Using KNN imputation
Using random forest for imputation
Summary
Section 2 – Preprocessing, Feature Selection, and Sampling
Chapter 4: Encoding, Transforming, and Scaling Features
Technical requirements
Creating training datasets and avoiding data leakage
Removing redundant or unhelpful features
Encoding categorical features
One-hot encoding
Ordinal encoding
Encoding categorical features with medium or high cardinality
Feature hashing
Using mathematical transformations
Feature binning
Equal-width and equal-frequency binning
K-means binning
Feature scaling
Summary
Chapter 5: Feature Selection
Technical requirements
Selecting features for classification models
Mutual information classification for feature selection with a categorical target
ANOVA F-value for feature selection with a categorical target
Selecting features for regression models
F-tests for feature selection with a continuous target
Mutual information for feature selection with a continuous target
Using forward and backward feature selection
Using forward feature selection
Using backward feature selection
Using exhaustive feature selection
Eliminating features recursively in a regression model
Eliminating features recursively in a classification model
Using Boruta for feature selection
Using regularization and other embedded methods
Using L1 regularization
Using a random forest classifier
Using principal component analysis
Summary
Chapter 6: Preparing for Model Evaluation
Technical requirements
Measuring accuracy, sensitivity, specificity, and precision for binary classification
Examining CAP, ROC, and precision-sensitivity curves for binary classification
Constructing CAP curves
Plotting a receiver operating characteristic (ROC) curve
Plotting precision-sensitivity curves
Evaluating multiclass models
Evaluating regression models
Using K-fold cross-validation
Preprocessing data with pipelines
Summary
Section 3 – Modeling Continuous Targets with Supervised Learning
Chapter 7: Linear Regression Models
Technical requirements
Key concepts
Key assumptions of linear regression models
Linear regression and ordinary least squares
Linear regression and gradient descent
Using classical linear regression
Pre-processing the data for our regression model
Running and evaluating our linear model
Improving our model evaluation
Using lasso regression
Tuning hyperparameters with grid searches
Using non-linear regression
Regression with gradient descent
Summary
Chapter 8: Support Vector Regression
Technical requirements
Key concepts of SVR
Nonlinear SVR and the kernel trick
SVR with a linear model
Using kernels for nonlinear SVR
Summary
Chapter 9: K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosted Regression
Technical requirements
Key concepts for K-nearest neighbors regression
K-nearest neighbors regression
Key concepts for decision tree and random forest regression
Using random forest regression
Decision tree and random forest regression
A decision tree example with interpretation
Building and interpreting our actual model
Random forest regression
Using gradient boosted regression
Summary
Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning
Chapter 10: Logistic Regression
Technical requirements
Key concepts of logistic regression
Logistic regression extensions
Binary classification with logistic regression
Evaluating a logistic regression model
Regularization with logistic regression
Multinomial logistic regression
Summary
Chapter 11: Decision Trees and Random Forest Classification
Technical requirements
Key concepts
Using random forest for classification
Using gradient-boosted decision trees
Decision tree models
Implementing random forest
Implementing gradient boosting
Summary
Chapter 12: K-Nearest Neighbors for Classification
Technical requirements
Key concepts of KNN
KNN for binary classification
KNN for multiclass classification
KNN for letter recognition
Summary
Chapter 13: Support Vector Machine Classification
Technical requirements
Key concepts for SVC
Nonlinear SVM and the kernel trick
Multiclass classification with SVC
Linear SVC models
Nonlinear SVM classification models
SVMs for multiclass classification
Summary
Chapter 14: Naïve Bayes Classification
Technical requirements
Key concepts
Naïve Bayes classification models
Naïve Bayes for text classification
Summary
Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning
Chapter 15: Principal Component Analysis
Technical requirements
Key concepts of PCA
Feature extraction with PCA
Using kernels with PCA
Summary
Chapter 16: K-Means and DBSCAN Clustering
Technical requirements
The key concepts of k-means and DBSCAN clustering
Implementing k-means clustering
Implementing DBSCAN clustering
Summary
Index
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