Pro machine learning algorithms : a hands-on approach to implementing algorithms in Python and R /: a hands-on approach to implementing algorithms in Python and R. (2018)

• Record Type: Book
• Further Information: Note: V. Kishore Ayyadevara.
• Authors: Ayyadevara, V. Kishore
• Contents: 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. 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. 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. 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. 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.
• Publisher Details: Berkeley : Apress
• Publication Date: 2018
• Extent: 1 online resource (xxi, 372 pages), illustrations
• Subjects: 006.31; Computer science; Machine learning; Python (Computer program language); R (Computer program language); Machine learning; Python (Computer program language); R (Computer program language); Computers -- Programming Languages -- Python; Computers -- Database Management -- General; Computers -- Programming -- Open Source; Programming & scripting languages: general; Databases; Computer programming / software development; Artificial intelligence; Python (Computer program language); Big data; Open source software; Computer programming; Computers -- Intelligence (AI) & Semantics; Artificial intelligence; Electronic books
• Languages: English
• ISBNs: 9781484235645; 1484235649; 1484235630; 9781484235638
• Related ISBNs: 9781484235638
• Notes: Note: Online resource; title from PDF title page (EBSCO, viewed July 6, 2018).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.