Workshop: Machine Learning Fundamentals 201

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Women Who Code

Women Who Code Portland is holding a Machine Learning Fundamentals workshop. The goal of this workshop is to provide people with an introduction to machine learning using Python and open source libraries.
Attendees should be familiar with Jupyter Notebooks, the Pandas library, and also be comfortable with histograms, scatterplots, and boxplots.
Our events are geared towards women, but we welcome everyone who supports our mission of inspiring women to excel in technology careers and follows our code of conduct.
 
OUR INSTRUCTORS Ozlem Senlik Ozlem is a data scientist at Urban Airship where she builds predictive models for digital marketing. She holds a PhD in Electrical and Computer Engineering from Duke University and has a background in photonics and microsystems. <A HREF="https://www.linkedin.com/in/ozlem-senlik-66b40154/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Ozlem's LinkedIn</A>
Lisa Orr Lisa works as a data scientist at Urban Airship where she builds predictive models on top of digital engagement data. She has a PhD in Psychology from the University of St. Andrews where she studied gestural communication of bonobos. <A HREF="https://www.linkedin.com/in/lisaorr/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Lisa's LinkedIn</A>
 
OUR TA’s Alekhya Vemavarapu Alekhya is a member of Enterprise Data Analytics at Nike where she does data engineering & analysis on Asia Pacific region. She holds a Masters degree in Data Sciences from IIIT-H, India and has a background in Computer science.
Siggy Hinds Siggy is a front end software engineer on the Web team at Urban Airship. She is a recent CS post-bac grad from PSU and also holds a BA in Anthropology from USC. In her free time (between work and being a mom) she studies various programming topics.
Dipti Muni Dipti is a Senior Data Analyst at Fiserv. She has recently received Database Design & SQL Certificate and has done training in Python for Data Science at Thinkful. She is also an active member at Portland Data Science Group.
 
AGENDA 9:30 - 10:15 Section 1: What is machine learning? 10:15 - 10:30 Break 10:30 - 11:10 Section 2: Machine Learning Techniques I 11:10 - 12:00 Section 3: Machine Learning Techniques II 12:00 - 1:00 Lunch 1:00 - 1:30 Lecture: Getting into data science 1:30 - 2:30 Section 4: Model Selection and Evaluation 2:30 - 2:45 Break 2:45 - 4:00 Lab 4: Build ML model predicting breast cancer 4:00 - 4:30 Discussion: Discussion on how to leverage today's work into your own projects *Curriculum is subject to changes
 
EVENT COST The event cost is $15 for participants and includes lunch. Fee is collected via Meetup. If you are a student, under-employed, or in need of financial assistance, we have full scholarships available for this event. Please submit an application <A HREF="https://goo.gl/forms/ANmXwj5Uyaqv9WLn2" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">here</A>.
There will be no refunds for this event. If you cannot attend, you can email us to transfer the ticket to another attendee, or we can add it to our scholarship pool.
 
CODE OF CONDUCT Women Who Code (WWCode) is dedicated to providing an empowering experience for everyone who participates in or supports our community, regardless of gender, gender identity and expression, sexual orientation, ability, physical appearance, body size, race, ethnicity, age, religion, socioeconomic status, caste, or creed. Our events are intended to inspire women to excel in technology careers, and anyone who is there for this purpose is welcome. Because we value the safety and security of our members and strive to have an inclusive community, we do not tolerate harassment of members or event participants in any form.
Our <A HREF="https://www.womenwhocode.com/codeofconduct" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Code of Conduct</A> applies to all events run by Women Who Code, Inc. If you would like to report an incident or contact our leadership team, please reach out to one of our volunteers or submit an <A HREF="http://bit.ly/wwcode-incident-report" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">incident report form</A>.

Alchemy Code Lab

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Workshop: Machine Learning Fundamentals 201

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Workshop: Machine Learning Fundamentals 201

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