Abstract: Python leads as a top machine learning solution – thanks largely to its extensive battery of powerful open source machine learning libraries. It’s also one of the most important, powerful programming languages in general. Python provides a great way for machine learning newcomers to begin their hands-on practice, or for experienced practitioners to augment their growing battery of tools.
By completing this hands-on workshop, you will develop an understanding of machine learning concepts and methodologies and learn how to fit, tune, and evaluate the predictive performance of a variety of parametric and non-parametric models for classification and regression. You will become familiar with how to preprocess data, build, tune, and cross-validate predictive models, and make predictions with the models in Python.
Session Outline:
The training agenda covers the end-to-end machine learning process, including loading and preprocessing data, building, tuning, and comparing classification and regression models, making predictions, and reporting on model performance:
Lesson 1: Data preprocessing
Familiarize yourself with common data preprocessing tasks and the Python syntax necessary to perform them. At the end of this lesson, you will be able to handle missing values, standardize and normalize numeric predictors, and deal with categorical predictors in Python.
Lesson 2: Model evaluation and tuning
Familiarize yourself with cross-validation and parameter tuning strategies and the Python syntax necessary to perform them. At the end of this lesson, you will be able to split your data into train and test sets, perform leave-one-out and K-fold cross-validation, and optimize parameters in Python.
Lesson 3: Classification
Familiarize yourself with parametric and non-parametric classification models and the Python syntax necessary to build and use them. At the end of this lesson, you will be able to build, tune, evaluate, compare, and predict with these classification models in Python.
Lesson 4: Ensemble methods
Familiarize yourself with ensemble models and the Python syntax necessary to build and use them. At the end of this lesson, you will be able to build, tune, evaluate, compare, and predict with these ensemble models in Python.
Lesson 5: Regression
Familiarize yourself with parametric and non-parametric regression models and the Python syntax necessary to build and use them. At the end of this lesson, you will be able to build, tune, evaluate, compare, and predict with these regression models in Python.
Background Knowledge:
Prior experience programming in any language (for machine learning or otherwise) and fundamental knowledge of machine learning concepts. This workshop can serve as your first experience executing on machine learning hands-on – or, if you already have such experience with a language or platform other than Python, this workshop will serve to facilitate your "lateral move" to Python.
Bio: Clinton Brownley, Ph.D., is a data scientist at Meta (formerly Facebook), where he’s responsible for a variety of analytics projects designed to empower employees to do their best work. Prior to this role, he was a data scientist at WhatsApp, working to improve messaging and VoIP calling performance and reliability. Before WhatsApp, he worked on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions at Facebook.
As an avid student and teacher of modern data analysis and visualization techniques, Clinton teaches a graduate course in interactive data visualization for UC Berkeley's MIDS program, taught a short-term graduate course in regression analysis and machine learning workshop for NYU's A3SR program, leads an annual machine learning in Python workshop, and is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis”.
Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.
