| Document Type
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BL
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| Record Number
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851065
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| Main Entry
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Ayyadevara, V. Kishore
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| Title & Author
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Pro machine learning algorithms : : a hands-on approach to implementing algorithms in Python and R /\ V. Kishore Ayyadevara.
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| Publication Statement
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[Berkeley] :: Apress,, 2018.
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| Page. NO
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1 online resource (xxi, 372 pages) :: illustrations
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| ISBN
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1484235630
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: 1484235649
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: 9781484235638
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: 9781484235645
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9781484235638
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| Bibliographies/Indexes
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Includes bibliographical references.
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| Contents
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Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Basics of Machine Learning; Regression and Classification; Training and Testing Data; The Need for Validation Dataset; Measures of Accuracy; Absolute Error; Root Mean Square Error; Confusion Matrix; AUC Value and ROC Curve; Unsupervised Learning; Typical Approach Towards Building a Model; Where Is the Data Fetched From?; Which Data Needs to Be Fetched?; Pre-processing the Data; Feature Interaction; Feature Generation; Building the Models; Productionalizing the Models.
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Assumptions of Linear Regression; Summary; Chapter 3: Logistic Regression; Why Does Linear Regression Fail for Discrete Outcomes?; A More General Solution: Sigmoid Curve; Formalizing the Sigmoid Curve (Sigmoid Activation); From Sigmoid Curve to Logistic Regression; Interpreting the Logistic Regression; Working Details of Logistic Regression; Estimating Error; Scenario 1; Scenario 2; Least Squares Method and Assumption of Linearity; Running a Logistic Regression in R; Running a Logistic Regression in Python; Identifying the Measure of Interest; Common Pitfalls.
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Build, Deploy, Test, and Iterate; Summary; Chapter 2: Linear Regression; Introducing Linear Regression; Variables: Dependent and Independent; Correlation; Causation; Simple vs. Multivariate Linear Regression; Formalizing Simple Linear Regression; The Bias Term; The Slope; Solving a Simple Linear Regression; More General Way of Solving a Simple Linear Regression; Minimizing the Overall Sum of Squared Error; Solving the Formula; Working Details of Simple Linear Regression; Complicating Simple Linear Regression a Little; Arriving at Optimal Coefficient Values; Introducing Root Mean Squared Error.
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Running a Simple Linear Regression in R; Residuals; Coefficients; SSE of Residuals (Residual Deviance); Null Deviance; R Squared; F-statistic; Running a Simple Linear Regression in Python; Common Pitfalls of Simple Linear Regression; Multivariate Linear Regression; Working details of Multivariate Linear Regression; Multivariate Linear Regression in R; Multivariate Linear Regression in Python; Issue of Having a Non-significant Variable in the Model; Issue of Multicollinearity; Mathematical Intuition of Multicollinearity; Further Points to Consider in Multivariate Linear Regression.
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Time Between Prediction and the Event Happening; Outliers in Independent variables; Summary; Chapter 4: Decision Tree; Components of a Decision Tree; Classification Decision Tree When There Are Multiple Discrete Independent Variables; Information Gain; Calculating Uncertainty: Entropy; Calculating Information Gain; Uncertainty in the Original Dataset; Measuring the Improvement in Uncertainty; Which Distinct Values Go to the Left and Right Nodes; Gini Impurity; Splitting Sub-nodes Further; When Does the Splitting Process Stop?; Classification Decision Tree for Continuous Independent Variables.
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| Abstract
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Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers. You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. You will: Get an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building models Implement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithm Gain the tricks of ensemble learning to build more accurate models Discover the basics of programming in R/Python and the Keras framework for deep learning.
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| Subject
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Machine learning.
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Python (Computer program language)
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R (Computer program language)
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Artificial intelligence.
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Computer programming-- software development.
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COMPUTERS-- Programming Languages-- Python.
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Databases.
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Machine learning.
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Programming scripting languages: general.
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Python (Computer program language)
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R (Computer program language)
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| Dewey Classification
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006.31
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| LC Classification
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Q325.5
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