[This event will be live online. The link will be provided to attendees during the morning of the event.]
As data scientists, your time is expensive but computation time is cheap. Matthew Emery will share how to leverage Python libraries to automate basic feature engineering and model selection tasks. Spend more time on the hard problems and let your computer find the best model for your data.
5:00 - 5:45 - Presentation / Talk
5:45 - 6:00 - Wind-down and Q&A
About the Speaker
Matthew Emery is a former software developer whose path as a data scientist began when he realized much of his day-to-day work could be automated. After work, he taught myself how to program Python and wrote scripts that saved his team nearly 40 working hours a week. For his contributions, he was promoted to technical lead.
In his new capacity, he has spearheaded projects involving backend development and data analysis.
Matthew is an active participant in the data science competition website Kaggle, has presented an in-depth talk on detecting galaxy morphology using convolutional neural networks (http://matthewemery.ca/kaggle-galaxy-presentation/#/), and has written about machine learning XGBoost (http://matthewemery.ca/Why-Kagglers-Love-XGBoost/).
Metis (thisismetis.com) accelerates careers in data science by providing full-time immersive bootcamps, evening part-time professional development courses, online resources, and corporate programs based in Seattle, New York, Chicago, and San Francisco.
Brought to you by Kaplan, Metis focuses primarily on Python, machine learning, data visualization, deep learning, big data processing, statistical foundations, and more. Students and alumni of the bootcamp program receive continuous support from our career advisors, empowering them to pursue a successful career in the fast-growing field of data science.
Learn more about us at https://thisismetis.com.
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