Global Classroom Python Machine Learning

About Girls in TechGirls in Tech works to erase the gender gap in tech. Today, every industry is a tech industry, with a need for people of all skills and backgrounds. We offer education and experiences to help people discover their unique superpower and hone it. We aim to see every woman accepted, confident, and valued in tech—just as they are.MissionEliminate the gender gap in tech by providing experiences and educational opportunities that leave people feeling:Inspired to pursue their dreamsEmpowered to be their best selvesConnected to a supportive communityPrepared to pursue modern careersConfident throughout their journey

Girls in Tech

Build your Machine Learning capability and learn to solve real-world problems using Python.

Women in tech – Take your Python skills to the next level in this interactive program, run in partnership with S&amp;P Global!To be successful in this Global Classroom program you should have 3-6 months of experience with Python and an understanding of programming concepts (with at least high school level knowledge of probability and linear algebra). Or be a graduate of Global Classroom Level 1.Over four sessions, you’ll complete fun, hands-on, individual, and group-based exercises, followed by insightful discussions.<h3>Course Details</h3>Session 1: Get a refresher on the basics of preparing data for machine learning using Pandas from the global Classroom Level 1, simulation methods, and powerful methods of statistical modeling and inference.You&#x27;ll learn:<ul><li>Introduction to NumPy for manipulating numerical data (vectors and matrices)</li><li>Refresher on reading, cleaning, and preparing data for ML using Pandas</li><li>Simulation and statistical inference with scipy.stats and scikit-learn; applications</li></ul>Session 2: Learn how to use clustering algorithms in sci-kit-learn for various kinds of data.<ul><li>Intuition behind spatial clustering</li><li>Clustering other kinds of data: high-dimensional, time-series, text</li><li>Clustering applications: customer profiling; image compression; anomaly detection</li></ul>Session 3: Explore classification algorithms and some core concepts of machine learning.<ul><li>Classification with scikit-learn</li><li>Cross-validation; overfitting</li><li>Model selection and diagnostic tools: yellowbrick</li></ul>Session 4: Dive into linear and nonlinear regression, and further concepts of machine learning like feature engineering.<ul><li>Regression with scikit-learn</li><li>Applications of regression. (Examples: nutrition, healthcare, and epidemiology)</li><li>Feature engineering and selection; case studies</li></ul>Session Dates: May 31, June 7, June 14, and June 21 (US timezones)Sessions Time: Tuesdays, 3–7 pm PSTBetween weekly sessions, we suggest 4–6 hours of project work each week as a guideline. You’ll form groups and apply new skills to self-selected project topics.Total Time Commitment: 8–10 hours per week.Sign up to get started!
*** Disclaimer - tickets are non-refundable!

<h3>About the instructor</h3>Edward Schofield is well-known in the Python community as a former release manager of SciPy, co-author of several of its modules, and the author of the widely used future package. He has consulted to or trained over 3000 people from dozens of organizations in data science and Python, and regularly presents at workshops and conferences in Python and data analytics internationally.

Global Classroom Python Machine Learning

More events from Girls in Tech

Follow organizers to get events picked for you

Still looking for the right event?

Explore all online events to browse and filter by date, category, and more.

Global Classroom Python Machine Learning

More events from Girls in Tech

Follow organizers to get events picked for you

You might also like...

Browse more events with different dates, prices, and formats to find your next great experience.

Still looking for the right event?

Explore all online events to browse and filter by date, category, and more.