
Data Cleaning and Exploration with Machine Learning
Description
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Person
Michael Walker has worked as a data analyst for over 30 years at a variety of educational institutions. He is currently the CIO at College Unbound in Providence, Rhode Island, in the United States. He has also taught data science, research methods, statistics, and computer programming to undergraduates since 2006.
Content
Examining the Distribution of Features and Targets
Examining Bivariate and Multivariate Relationships between Features and Targets
Identifying and Fixing Missing Values
Encoding, Transforming, and Scaling Features
Feature Selection
Preparing for Model Evaluation
Linear Regression Models
Support Vector Regression
K-Nearest Neighbor, Decision Tree, Random Forest and Gradient Boosted Regression
Logistic Regression
Decision Trees and Random Forest Classification
K-Nearest Neighbors for Classification
Support Vector Machine Classification
Naive Bayes Classification
Principal Component Analysis
K-Means and DBSCAN Clustering
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File format: ePUB
Copy protection: without DRM (Digital Rights Management)
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- Tablet/Smartphone (Android; iOS): Install the free app Adobe Digital Editions or the app PocketBook (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (not Kindle).
The file format ePUB works well for novels and non-fiction books – i.e., 'flowing' text without complex layout. On an e-reader or smartphone, line and page breaks automatically adjust to fit the small displays.
This eBook does not use copy protection or Digital Rights Management
For more information, see our eBook Help page.