000			08140nam a22004933i 4500
001			EBC6126527
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005			20240724114134.0
006			m o d |
007			cr cnu||||||||
008			240724s2020 xx o ||||0 eng d
020			_a9781800208322 _q(electronic bk.)
020			_z9781800209046
035			_a(MiAaPQ)EBC6126527
035			_a(Au-PeEL)EBL6126527
035			_a(OCoLC)1143634009
040			_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ
050		4	_aQ325.5 .B384 2020
082	0		_a006.31
100	1		_aBateman, Blaine.
245	1	4	_aThe the Supervised Learning Workshop : _bA New, Interactive Approach to Understanding Supervised Learning Algorithms, 2nd Edition.
250			_a2nd ed.
264		1	_aBirmingham : _bPackt Publishing, Limited, _c2020.
264		4	_c©2020.
300			_a1 online resource (531 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
505	0		_aCover -- 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		_aExercise 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		_aLogistic 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		_aExercise 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			_aCut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms.
588			_aDescription based on publisher supplied metadata and other sources.
590			_aElectronic 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	_aMachine learning.
655		4	_aElectronic books.
700	1		_aJha, Ashish Ranjan.
700	1		_aJohnston, Benjamin.
700	1		_aMathur, Ishita.
776	0	8	_iPrint version: _aBateman, Blaine _tThe the Supervised Learning Workshop _dBirmingham : Packt Publishing, Limited,c2020 _z9781800209046
797	2		_aProQuest (Firm)
856	4	0	_uhttps://ebookcentral.proquest.com/lib/orpp/detail.action?docID=6126527 _zClick to View
999			_c16460 _d16460