The the Supervised Learning Workshop : (Record no. 16460)
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| fixed length control field | 08140nam a22004933i 4500 |
| 001 - CONTROL NUMBER | |
| control field | EBC6126527 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240724114134.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240724s2020 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781800208322 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9781800209046 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (MiAaPQ)EBC6126527 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (Au-PeEL)EBL6126527 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1143634009 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MiAaPQ |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | MiAaPQ |
| Modifying agency | MiAaPQ |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | Q325.5 .B384 2020 |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.31 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Bateman, Blaine. |
| 245 14 - TITLE STATEMENT | |
| Title | The the Supervised Learning Workshop : |
| Remainder of title | A New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd ed. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Birmingham : |
| Name of producer, publisher, distributor, manufacturer | Packt Publishing, Limited, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2020. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | ©2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (531 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Cover -- FM -- Copyright -- Table of Contents -- Preface -- Chapter 1: Fundamentals -- Introduction -- When to Use Supervised Learning -- Python Packages and Modules -- Loading Data in Pandas -- Exercise 1.01: Loading and Summarizing the Titanic Dataset -- Exercise 1.02: Indexing and Selecting Data -- Exercise 1.03: Advanced Indexing and Selection -- Pandas Methods -- Exercise 1.04: Using the Aggregate Method -- Quantiles -- Lambda Functions -- Exercise 1.05: Creating Lambda Functions -- Data Quality Considerations -- Managing Missing Data -- Class Imbalance -- Low Sample Size -- Activity 1.01: Implementing Pandas Functions -- Summary -- Chapter 2: Exploratory Data Analysis and Visualization -- Introduction -- Exploratory Data Analysis (EDA) -- Summary Statistics and Central Values -- Exercise 2.01: Summarizing the Statistics of Our Dataset -- Missing Values -- Finding Missing Values -- Exercise 2.02: Visualizing Missing Values -- Imputation Strategies for Missing Values -- Exercise 2.03: Performing Imputation Using Pandas -- Exercise 2.04: Performing Imputation Using Scikit-Learn -- Exercise 2.05: Performing Imputation Using Inferred Values -- Activity 2.01: Summary Statistics and Missing Values -- Distribution of Values -- Target Variable -- Exercise 2.06: Plotting a Bar Chart -- Categorical Data -- Exercise 2.07: Identifying Data Types for Categorical Variables -- Exercise 2.08: Calculating Category Value Counts -- Exercise 2.09: Plotting a Pie Chart -- Continuous Data -- Skewness -- Kurtosis -- Exercise 2.10: Plotting a Histogram -- Exercise 2.11: Computing Skew and Kurtosis -- Activity 2.02: Representing the Distribution of Values Visually -- Relationships within the Data -- Relationship between Two Continuous Variables -- Pearson's Coefficient of Correlation -- Exercise 2.12: Plotting a Scatter Plot. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Exercise 2.13: Plotting a Correlation Heatmap -- Using Pairplots -- Exercise 2.14: Implementing a Pairplot -- Relationship between a Continuous and a Categorical Variable -- Exercise 2.15: Plotting a Bar Chart -- Exercise 2.16: Visualizing a Box Plot -- Relationship Between Two Categorical Variables -- Exercise 2.17: Plotting a Stacked Bar Chart -- Activity 2.03: Relationships within the Data -- Summary -- Chapter 3: Linear Regression -- Introduction -- Regression and Classification Problems -- The Machine Learning Workflow -- Business Understanding -- Data Understanding -- Data Preparation -- Modeling -- Evaluation -- Deployment -- Exercise 3.01: Plotting Data with a Moving Average -- Activity 3.01: Plotting Data with a Moving Average -- Linear Regression -- Least Squares Method -- The Scikit-Learn Model API -- Exercise 3.02: Fitting a Linear Model Using the Least Squares Method -- Activity 3.02: Linear Regression Using the Least Squares Method -- Linear Regression with Categorical Variables -- Exercise 3.03: Introducing Dummy Variables -- Activity 3.03: Dummy Variables -- Polynomial Models with Linear Regression -- Exercise 3.04: Polynomial Models with Linear Regression -- Activity 3.04: Feature Engineering with Linear Regression -- Generic Model Training -- Gradient Descent -- Exercise 3.05: Linear Regression with Gradient Descent -- Exercise 3.06: Optimizing Gradient Descent -- Activity 3.05: Gradient Descent -- Multiple Linear Regression -- Exercise 3.07: Multiple Linear Regression -- Summary -- Chapter 4: Autoregression -- Introduction -- Autoregression Models -- Exercise 4.01: Creating an Autoregression Model -- Activity 4.01: Autoregression Model Based on Periodic Data -- Summary -- Chapter 5: Classification Techniques -- Introduction -- Ordinary Least Squares as a Classifier -- Exercise 5.01: Ordinary Least Squares as a Classifier. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Logistic Regression -- Exercise 5.02: Logistic Regression as a Classifier - Binary Classifier -- Exercise 5.03: Logistic Regression - Multiclass Classifier -- Activity 5.01: Ordinary Least Squares Classifier - Binary Classifier -- Select K Best Feature Selection -- Exercise 5.04: Breast Cancer Diagnosis Classification Using Logistic Regression -- Classification Using K-Nearest Neighbors -- Exercise 5.05: KNN Classification -- Exercise 5.06: Visualizing KNN Boundaries -- Activity 5.02: KNN Multiclass Classifier -- Classification Using Decision Trees -- Exercise 5.07: ID3 Classification -- Classification and Regression Tree -- Exercise 5.08: Breast Cancer Diagnosis Classification Using a CART Decision Tree -- Activity 5.03: Binary Classification Using a CART Decision Tree -- Artificial Neural Networks -- Exercise 5.09: Neural Networks - Multiclass Classifier -- Activity 5.04: Breast Cancer Diagnosis Classification Using Artificial Neural Networks -- Summary -- Chapter 6: Ensemble Modeling -- Introduction -- One-Hot Encoding -- Exercise 6.01: Importing Modules and Preparing the Dataset -- Overfitting and Underfitting -- Underfitting -- Overfitting -- Overcoming the Problem of Underfitting and Overfitting -- Bagging -- Bootstrapping -- Exercise 6.02: Using the Bagging Classifier -- Random Forest -- Exercise 6.03: Building the Ensemble Model Using Random Forest -- Boosting -- Adaptive Boosting -- Exercise 6.04: Implementing Adaptive Boosting -- Gradient Boosting -- Exercise 6.05: Implementing GradientBoostingClassifier to Build an Ensemble Model -- Stacking -- Exercise 6.06: Building a Stacked Model -- Activity 6.01: Stacking with Standalone and Ensemble Algorithms -- Summary -- Chapter 7: Model Evaluation -- Introduction -- Importing the Modules and Preparing Our Dataset -- Evaluation Metrics -- Regression Metrics. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Exercise 7.01: Calculating Regression Metrics -- Classification Metrics -- Numerical Metrics -- Curve Plots -- Exercise 7.02: Calculating Classification Metrics -- Splitting a Dataset -- Hold-Out Data -- K-Fold Cross-Validation -- Sampling -- Exercise 7.03: Performing K-Fold Cross-Validation with Stratified Sampling -- Performance Improvement Tactics -- Variation in Train and Test Errors -- Learning Curve -- Validation Curve -- Hyperparameter Tuning -- Exercise 7.04: Hyperparameter Tuning with Random Search -- Feature Importance -- Exercise 7.05: Feature Importance Using Random Forest -- Activity 7.01: Final Test Project -- Summary -- Appendix -- Index. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning. |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Jha, Ashish Ranjan. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Johnston, Benjamin. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Mathur, Ishita. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Main entry heading | Bateman, Blaine |
| Title | The the Supervised Learning Workshop |
| Place, publisher, and date of publication | Birmingham : Packt Publishing, Limited,c2020 |
| International Standard Book Number | 9781800209046 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) | |
| Corporate name or jurisdiction name as entry element | ProQuest (Firm) |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=6126527">https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=6126527</a> |
| Public note | Click to View |
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