Workshop – Machine Learning with Python: A Hands-On Introduction
Monday, June 1, 2020 – Livestream
Full-day: 8:00am – 3:00pm PDT
Intended Audience: Practitioners who wish to learn how to execute on machine learning with Python.
Knowledge Level: 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.
Attendees will receive a full recording of the workshop
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.
Note regarding deep learning. Python’s popularity has recently grown even further since it is the most common way to access leading deep learning solutions such as TensorFlow. Note that this workshop day does not cover deep learning, since it serves first-time users by covering a broader, foundational range of traditional machine learning methods. However, this training does provide helpful groundwork for the “Hands-On Deep Learning in the Cloud” workshop scheduled for later in the same week.
During this full-day training workshop, instructor Clinton Brownley – a data scientist at WhatsApp and formerly Facebook, where he gained extensive experience leading internal machine learning trainings – will take you on your first steps with Python, guiding you through challenging hands-on exercises to employ various machine learning capabilities within Python and apply them on real world datasets.
A comprehensive training. 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. Topics include:
- Data preprocessing
- Model tuning
- Model evaluation
- Ensemble methods
Diverse application areas. This workshop’s hands-on exercises cover various applications of predictive modeling that serve to mitigate harm and save money, including: gambling, hospital readmissions, nefarious actor detection, time to failure, and hotel bookings.
Bring your laptop with Python pre-installed. Workshop participants are required to bring their own laptops for use during this hands-on workshop with Python version ≥3.x installed. The primary libraries for the workshop are pandas, scikit-learn, and matplotlib, but specific examples may rely on other libraries, so participants should also install seaborn, pymc3, and jupyter.
Pre-install instructions. The easiest way to have both a compatible version of Python as well as all these required libraries on your laptop is to install the Anaconda Distribution of Python. Please be sure to do prior to the workshop day.
- Workshop starts at 8:00am PDT
- AM Break from 9:30 – 9:45am PDT
- Lunch Break from 11:00 – 11:45am PDT
- PM Break: 1:15pm – 1:30pm PDT
- Workshops ends at 3:00pm PDT
Clinton Brownley, Data Scientist, WhatsApp
Clinton Brownley, Ph.D., is a data scientist at WhatsApp, where he’s responsible for a variety of analytics projects designed to improve messaging and VoIP calling performance and reliability. Before WhatsApp, Clinton was a data scientist at Facebook, working on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions. As an avid student and teacher of modern analytics techniques, Clinton is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis,” and also teaches Python programming and data science courses at Facebook and in the Bay Area. 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.