Introduction to Machine Learning with Python: (Record no. 752090)

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003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251106130837.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
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020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789352134571
040 ## - CATALOGING SOURCE
Transcribing agency KUL
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Item number MUL/I
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Muller, Andreas C & Guido, Sarah
9 (RLIN) 2696
245 ## - TITLE STATEMENT
Title Introduction to Machine Learning with Python:
Remainder of title A Guide for Data Scientists/
Statement of responsibility, etc. Andreas C Muller & Sarah Guido
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Mumbai:
Name of publisher, distributor, etc. Shroff Publishers & Distributors Pvt. Ltd.,
Date of publication, distribution, etc. 2024.
300 ## - PHYSICAL DESCRIPTION
Extent xii,378p.:
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
General subdivision 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) 213 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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 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-of-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
9 (RLIN) 4306
942 ## - ADDED ENTRY ELEMENTS (KOHA)
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    Dewey Decimal Classification     Dept. of Optoelectronics Dept. of Optoelectronics New Materials Shelf 07/10/2025   006.31 MUL/I DOP3740 07/10/2025 07/10/2025 Reference Reference Only    
    Dewey Decimal Classification     Dept. of Optoelectronics Dept. of Optoelectronics New Materials Shelf 07/10/2025   006.31 MUL/I;1 DOP3741 07/10/2025 07/10/2025 Book      
    Dewey Decimal Classification     Dept. of Optoelectronics Dept. of Optoelectronics New Materials Shelf 07/10/2025 1 006.31 MUL/I;2 DOP3742 03/11/2025 07/10/2025 Book   18/11/2025 03/11/2025
    Dewey Decimal Classification     Dept. of Optoelectronics Dept. of Optoelectronics New Materials Shelf 07/10/2025 2 006.31 MUL/I;3 DOP3743 06/03/2026 07/10/2025 Book   21/03/2026 06/03/2026
    Dewey Decimal Classification     Dept. of Optoelectronics Dept. of Optoelectronics New Materials Shelf 07/10/2025   006.31 MUL/I;4 DOP3744 07/10/2025 07/10/2025 Book