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Spis treści

Introduction to Machine Learning with Python. A Guide for Data Scientists eBook -- spis treści

• Preface
  • Who Should Read This Book
  • Why We Wrote This Book
  • Navigating This Book
  • Online Resources
  • Conventions Used in This Book
  • Using Code Examples
  • Safari Books Online
  • How to Contact Us
  • Acknowledgments
    • From Andreas
    • From Sarah
• 1. Introduction
  • Why Machine Learning?
    • Problems Machine Learning Can Solve
    • Knowing Your Task and Knowing Your Data
  • Why Python?
  • scikit-learn
    • Installing scikit-learn
  • Essential Libraries and Tools
    • Jupyter Notebook
    • NumPy
    • SciPy
    • matplotlib
    • pandas
    • mglearn
  • Python 2 Versus Python 3
  • Versions Used in this Book
  • A First Application: Classifying Iris Species
    • Meet the Data
    • Measuring Success: Training and Testing Data
    • First Things First: Look at Your Data
    • Building Your First Model: k-Nearest Neighbors
    • Making Predictions
    • Evaluating the Model
  • Summary and Outlook
• 2. Supervised Learning
  • Classification and Regression
  • Generalization, Overfitting, and Underfitting
    • Relation of Model Complexity to Dataset Size
  • Supervised Machine Learning Algorithms
    • Some Sample Datasets
    • k-Nearest Neighbors
      • k-Neighbors classification
      • Analyzing KNeighborsClassifier
      • k-neighbors regression
      • Analyzing KNeighborsRegressor
      • Strengths, weaknesses, and parameters
    • Linear Models
      • Linear models for regression
      • Linear regression (aka ordinary least squares)
      • Ridge regression
      • Lasso
      • Linear models for classification
      • Linear models for multiclass classification
      • Strengths, weaknesses, and parameters
    • Naive Bayes Classifiers
      • Strengths, weaknesses, and parameters
    • Decision Trees
      • Building decision trees
      • Controlling complexity of decision trees
      • Analyzing decision trees
      • Feature importance in trees
      • Strengths, weaknesses, and parameters
    • Ensembles of Decision Trees
      • Random forests
        • Building random forests
        • Analyzing random forests
        • Strengths, weaknesses, and parameters
      • Gradient boosted regression trees (gradient boosting machines)
        • Strengths, weaknesses, and parameters
    • Kernelized Support Vector Machines
      • Linear models and nonlinear features
      • The kernel trick
      • Understanding SVMs
      • Tuning SVM parameters
      • Preprocessing data for SVMs
      • Strengths, weaknesses, and parameters
    • Neural Networks (Deep Learning)
      • The neural network model
      • Tuning neural networks
      • Strengths, weaknesses, and parameters
        • Estimating complexity in neural networks
  • Uncertainty Estimates from Classifiers
    • The Decision Function
    • Predicting Probabilities
    • Uncertainty in Multiclass Classification
  • Summary and Outlook
• 3. Unsupervised Learning and Preprocessing
  • Types of Unsupervised Learning
  • Challenges in Unsupervised Learning
  • Preprocessing and Scaling
    • Different Kinds of Preprocessing
    • Applying Data Transformations
    • Scaling Training and Test Data the Same Way
    • The Effect of Preprocessing on Supervised Learning
  • Dimensionality Reduction, Feature Extraction, and Manifold Learning
    • Principal Component Analysis (PCA)
      • Applying PCA to the cancer dataset for visualization
      • Eigenfaces for feature extraction
    • Non-Negative Matrix Factorization (NMF)
      • Applying NMF to synthetic data
      • Applying NMF to face images
    • Manifold Learning with t-SNE
  • Clustering
    • k-Means Clustering
      • Failure cases of k-means
      • Vector quantization, or seeing k-means as decomposition
    • Agglomerative Clustering
      • Hierarchical clustering and dendrograms
    • DBSCAN
    • Comparing and Evaluating Clustering Algorithms
      • Evaluating clustering with ground truth
      • Evaluating clustering without ground truth
      • Comparing algorithms on the faces dataset
        • Analyzing the faces dataset with DBSCAN
        • Analyzing the faces dataset with k-means
        • Analyzing the faces dataset with agglomerative clustering
    • Summary of Clustering Methods
  • Summary and Outlook
• 4. Representing Data and Engineering Features
  • Categorical Variables
    • One-Hot-Encoding (Dummy Variables)
      • Checking string-encoded categorical data
    • Numbers Can Encode Categoricals
  • Binning, Discretization, Linear Models, and Trees
  • Interactions and Polynomials
  • Univariate Nonlinear Transformations
  • Automatic Feature Selection
    • Univariate Statistics
    • Model-Based Feature Selection
    • Iterative Feature Selection
  • Utilizing Expert Knowledge
  • Summary and Outlook
• 5. Model Evaluation and Improvement
  • Cross-Validation
    • Cross-Validation in scikit-learn
    • Benefits of Cross-Validation
    • Stratified k-Fold Cross-Validation and Other Strategies
      • More control over cross-validation
      • Leave-one-out cross-validation
      • Shuffle-split cross-validation
      • Cross-validation with groups
  • Grid Search
    • Simple Grid Search
    • The Danger of Overfitting the Parameters and the Validation Set
    • Grid Search with Cross-Validation
      • Analyzing the result of cross-validation
      • Search over spaces that are not grids
      • Using different cross-validation strategies with grid search
      • Nested cross-validation
      • Parallelizing cross-validation and grid search
  • Evaluation Metrics and Scoring
    • Keep the End Goal in Mind
    • Metrics for Binary Classification
      • Kinds of errors
      • Imbalanced datasets
      • Confusion matrices
        • Relation to accuracy
        • Precision, recall, and f-score
      • Taking uncertainty into account
      • Precision-recall curves and ROC curves
      • Receiver operating characteristics (ROC) and AUC
    • Metrics for Multiclass Classification
    • Regression Metrics
    • Using Evaluation Metrics in Model Selection
  • Summary and Outlook
• 6. Algorithm Chains and Pipelines
  • Parameter Selection with Preprocessing
  • Building Pipelines
  • Using Pipelines in Grid Searches
  • The General Pipeline Interface
    • Convenient Pipeline Creation with make_pipeline
    • Accessing Step Attributes
    • Accessing Attributes in a Grid-Searched Pipeline
  • Grid-Searching Preprocessing Steps and Model Parameters
  • Grid-Searching Which Model To Use
  • Summary and Outlook
• 7. Working with Text Data
  • Types of Data Represented as Strings
  • Example Application: Sentiment Analysis of Movie Reviews
  • Representing Text Data as a Bag of Words
    • Applying Bag-of-Words to a Toy Dataset
    • Bag-of-Words for Movie Reviews
  • Stopwords
  • Rescaling the Data with tfidf
  • Investigating Model Coefficients
  • Bag-of-Words with More Than One Word (n-Grams)
  • Advanced Tokenization, Stemming, and Lemmatization
  • Topic Modeling and Document Clustering
    • Latent Dirichlet Allocation
  • Summary and Outlook
• 8. Wrapping Up
  • Approaching a Machine Learning Problem
    • Humans in the Loop
  • From Prototype to Production
  • Testing Production Systems
  • Building Your Own Estimator
  • Where to Go from Here
    • Theory
    • Other Machine Learning Frameworks and Packages
    • Ranking, Recommender Systems, and Other Kinds of Learning
    • Probabilistic Modeling, Inference, and Probabilistic Programming
    • Neural Networks
    • Scaling to Larger Datasets
    • Honing Your Skills
  • Conclusion
• Index