Machine learning models provide a fast and flexible way to build predictive models of the world, and are used for tasks ranging from predicting supply chain availability to optimizing the placement of advertisements. The tools discussed in this class are fast becoming industry standards in bioscience, finance, geology, manufacturing, and marketing.
The Machine Learning Mastery Workshop is 3 days of individualized coaching in the use of scikit-learn to predict country-specific risk of famine using satellite imagery, intelligence reports, and historical climate records. Students will return to work the same week ready to apply advanced learning algorithms to business cases in their own industries.
The course begins with a conceptual introduction to machine learning algorithms. This is followed by an introduction to the implementation of estimators in scikit-learn and best practices for using them.
The rest of the course is focused around specific feature sources, and for each progresses through a short introductory lecture followed by three exercises of progressive difficulty, starting with standard and well-behaved cases, and ending with real-world and realistically problematic case studies.
Throughout, the focus of the course is on building deep conceptual understanding, exhaustive practical experience, and covering common mistakes and edge cases. Intermingled in the machine learning material will be short discussions of helpful and diagnostic data visualizations.
Fill out the form below to have Enthought’s full set of 3 Machine Learning with Scikit-Learn cheat sheets sent directly to you (free!).
Enthought instructors have doctorates in scientific fields such as physics, engineering, computer science, and mathematics, and all have extensive experience through research and consulting in applying Python to solve complex problems across a range of industries, allowing them to bring their real world experience to the classroom every day. Enthought instructors possess professional, first-hand experience with the tools and technologies covered in our courses.
Knowledge of programming in the Python standard language (data structures, control flow, assignment, functions, and package access) and familiarity with array programming in NumPy is required. Familiarity with the Pandas and matplotlib libraries is also required (DataFrames, indexing, plot grids). Knowledge of general data analysis techniques and basic statistics (mean, standard deviation, correlation, etc.) is strongly recommended.
Individuals who have taken Enthought’s Python Foundations, Python for Scientists and Engineers, Python for Data Science, or Python for Data Analysis classes will have met the prerequisites for the course.
Onsite corporate classes are also available. Discounts are available for 3 or more attendees and academics currently at a degree-granting institution. Contact us to learn more.
|Where||When||Price (per person)||Register|
|Austin, TX||February 21-23, 2018||$1800|
|Houston, TX||April 18-20, 2018 (NOTE: this class will include a special 1/2 day module on oil & gas applications of machine learning on the 3rd day)||$1800||Contact us with the form to the right|
|Cambridge, UK||May 9-11, 2018||£1476||Contact us with the form to the right|
|Albuquerque, NM||May 21-23, 2018||$1800|
Have a question that isn’t answered here? Contact us or call 512.536.1057.