| Summary: | 4+ Hours of Video Instruction Code-along sessions move you through the development of classification and regression methods Overview Machine learning is moving from futuristic AI projects to data analysis on your desk. You need to go beyond following along in discussions to coding machine learning tasks. Developing Classification and Regression Systems LiveLessons (Machine Learning with Python for Everyone Series) Part 3 shows you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends. You will learn about fundamental classification and regression metrics like decision tree classifiers and regressors, support vector classifiers and regression, logistic regression, penalized regression, and discriminant analysis. You will see techniques for feature engineering, including scaling, discretization, and interactions. You will learn how to implement pipelines for more complex processing and nested cross-validation for tuning hyperparameters. About the Instructor Mark Fenner, PhD, has been teaching computing and mathematics to diverse adult audiences since 1999. His research projects have addressed design, implementation, and performance of machine learning and numerical algorithms, learning systems for security analysis of software repositories and intrusion detection, probabilistic models of protein function, and analysis and visualization of ecological and microscopy data. Mark continues to work across the data science spectrum from C, Fortran, and Python implementation to statistical analysis and visualization. He has delivered training and developed curriculum for Fortune 50 companies, boutique consultancies, and national-level research laboratories. Mark holds a PhD in Computer Science and owns Fenner Training and Consulting, LLC. Skill Level Beginner to Intermediate Learn How To Use fundamental classification methods including decision trees, support vector classifiers, logistic regression, and discriminant analysis Recognize bias and variability in classifiers Compare classifiers Use fundamental regression methods including penalized regression and regression trees Recognize bias and variability in regressors Manually engineer features through feature scaling, discretization, categorical coding, analysis of interactions, and target manipulations Tune hyperparameters Use nested cross-validation Develop pipelines Who Should Take This Course This course is a good fit for anyone who needs to...
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