Global Classroom Python Machine Learning

Women in tech – Take your Python skills to the next level in this interactive program, run in partnership with S&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.

Course Details

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'll learn:

• Introduction to NumPy for manipulating numerical data (vectors and matrices)
• Refresher on reading, cleaning, and preparing data for ML using Pandas
• Simulation and statistical inference with scipy.stats and scikit-learn; applications

Session 2: Learn how to use clustering algorithms in sci-kit-learn for various kinds of data.

• Intuition behind spatial clustering
• Clustering other kinds of data: high-dimensional, time-series, text
• Clustering applications: customer profiling; image compression; anomaly detection

Session 3: Explore classification algorithms and some core concepts of machine learning.

• Classification with scikit-learn
• Cross-validation; overfitting
• Model selection and diagnostic tools: yellowbrick

Session 4: Dive into linear and nonlinear regression, and further concepts of machine learning like feature engineering.

• Regression with scikit-learn
• Applications of regression. (Examples: nutrition, healthcare, and epidemiology)
• Feature engineering and selection; case studies

Session Dates: May 31, June 7, June 14, and June 21 (US timezones)

Sessions Time: Tuesdays, 3–7 pm PST

Between 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.

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About the instructor

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.

Sponsor