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Main Authors:	Müller, Andreas Christian (Author), Guido, Sarah (Author)
Format:	Book
Language:	English
Published:	© 2016
Edition:	First edition
Subjects:	Maschinelles Lernen Python > Programmiersprache
Links:	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028713402&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Item Description:	Hier auch später erschienene, unveränderte Nachdrucke (2017)
Physical Description:	xii, 378 Seiten Illustrationen
ISBN:	9781449369415 1449369413

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adam_text	Table of Contents Preface...................................................... --- ..............vii 1. Introduction.................................................. ..............1 Why Machine Learning? 1 Problems Machine Learning Can Solve 2 Knowing Your Task and Knowing Your Data 4 Why Python? 5 scikit-learn 5 Installing scikit-learn 6 Essential Libraries and Tools 7 Jupyter Notebook 7 NumPy 7 SciPy 8 matplotlib 9 pandas 10 mglearn 11 Python 2 Versus Python 3 12 Versions Used in this Book 12 A First Application: Classifying Iris Species 13 Meet the Data 14 Measuring Success: Training and Testing Data 17 First Things First: Look at Your Data 19 Building Your First Model: k-Nearest Neighbors 20 Making Predictions 22 Evaluating the Model 22 Summary and Outlook 23 jii 2. Supervised Learning.................................................... 25 Classification and Regression 25 Generalization, Overfitting, and Underfitting 26 Relation of Model Complexity to Dataset Size 29 Supervised Machine Learning Algorithms 29 Some Sample Datasets 30 k-Nearest Neighbors 35 Linear Models 45 Naive Bayes Classifiers 68 Decision Trees 70 Ensembles of Decision Trees 83 Kernelized Support Vector Machines 92 Neural Networks (Deep Learning) 104 Uncertainty Estimates from Classifiers 119 The Decision Function 120 Predicting Probabilities 122 Uncertainty in Multiclass Classification 124 Summary and Outlook 127 3. Unsupervised Learning and Preprocessing................................. 131 Types of Unsupervised Learning 131 Challenges in Unsupervised Learning 132 Preprocessing and Scaling 132 Different Kinds of Preprocessing 133 Applying Data Transformations 134 Scaling Training and Test Data the Same Way 136 The Effect of Preprocessing on Supervised Learning 138 Dimensionality Reduction, Feature Extraction, and Manifold Learning 140 Principal Component Analysis (PCA) 140 Non-Negative Matrix Factorization (NMF) 156 Manifold Learning with t-SNE 163 Clustering 168 k-Means Clustering 168 Agglomerative Clustering 182 DBSCAN 187 Comparing and Evaluating Clustering Algorithms 191 Summary of Clustering Methods 207 Summary and Outlook 208 4. Representing Data and Engineering Features..............................211 Categorical Variables 212 One-Hot-Encoding (Dummy Variables) 2.13 iv \| Table of Contents Numbers Can Encode Categoricals 218 Binning, Discretization, Linear Models, and Trees 220 Interactions and Polynomials 224 Univariate Nonlinear Transformations 232 Automatic Feature Selection 236 Univariate Statistics 236 Model-Based Feature Selection 238 Iterative Feature Selection 240 Utilizing Expert Knowledge 242 Summary and Outlook 250 5. Model Evaluation and Improvement.......................................... 251 Cross-Validation 252 Cross-Validation in scikit-learn 253 Benefits of Cross-Validation 254 Stratified k-Fold Cross-Validation and Other Strategies 254 Grid Search 260 Simple Grid Search 261 The Danger of Overfitting the Parameters and the Validation Set 261 Grid Search with Cross-Validation 263 Evaluation Metrics and Scoring 275 Keep the End Goal in Mind 275 Metrics for Binary Classification 276 Metrics for Multiclass Classification 296 Regression Metrics 299 Using Evaluation Metrics in Model Selection 300 Summary and Outlook 302 6. Algorithm Chains and Pipelines......................................... 305 Parameter Selection with Preprocessing 306 Building Pipelines 308 Using Pipelines in Grid Searches 309 The General Pipeline Interface 312 Convenient Pipeline Creation with make_pipeline 313 Accessing Step Attributes 314 Accessing Attributes in a Grid-Searched Pipeline 315 Grid-Searching Preprocessing Steps and Model Parameters 317 Grid-Searching Which Model To Use 319 Summary and Outlook 320 7. Working with Text Data.., 000000«00900 Ï00003000003009JQ OOOOOO Types of Data Represented as Strings 323 Table of Contents \| v Example Application: Sentiment Analysis of Movie Reviews 325 Representing Text Data as a Bag of Words 327 Applying Bag-oT Words to a Toy Dataset 329 Bag-of-Words for Movie Reviews 330 Stopwords 334 Rescaling the Data with tf—idf 336 Investigating Model Coefficients 338 Bag-of-Words with More Than One Word (n-Grams) 339 Advanced Tokenization, Stemming, and Lemmatization 344 Topic Modeling and Document Clustering 347 Latent Dirichlet Allocation 348 Summary and Outlook 355 8. Wrapping Up.......................................................... 357 Approaching a Machine Learning Problem 357 Humans in the Loop 358 From Prototype to Production 359 Testing Production Systems 359 Building Your Own Estimator 360 Where to Go from Here 361 Theory 361 Other Machine Learning Frameworks and Packages 362 Ranking, Recommender Systems, and Other Kinds of Learning 363 Probabilistic Modeling, Inference, and Probabilistic Programming 363 Neural Networks 364 Scaling to Larger Datasets 364 Honing Your Skills 365 Conclusion 366 Index................................................................... 367 vi j Table of Contents
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subject_GND	(DE-588)4193754-5 (DE-588)4434275-5
title	Introduction to machine learning with Python a guide for data scientists
title_auth	Introduction to machine learning with Python a guide for data scientists
title_exact_search	Introduction to machine learning with Python a guide for data scientists
title_full	Introduction to machine learning with Python a guide for data scientists Andreas C. Müller and Sarah Guido
title_fullStr	Introduction to machine learning with Python a guide for data scientists Andreas C. Müller and Sarah Guido
title_full_unstemmed	Introduction to machine learning with Python a guide for data scientists Andreas C. Müller and Sarah Guido
title_short	Introduction to machine learning with Python
title_sort	introduction to machine learning with python a guide for data scientists
title_sub	a guide for data scientists
topic	Maschinelles Lernen (DE-588)4193754-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd
topic_facet	Maschinelles Lernen Python Programmiersprache
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028713402&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
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