Learn the concepts and tools of an entire machine learning workflow using the scikit-learn library so that you can apply it to any future problem.
When: November 9-10, 9 AM - 5 PM
Where: 265 W. 37th St., New York, NY, 10018
General Admission: $299
Day 1: The Machine Learning Model with Scikit-Learn
- Use any machine learning model in scikit-learn with its Estimator objects
- Use the three-step process common to all machine learning Estimators - Import, Instantiate, Train
- Explore the details of linear regression, k-nearest neighbors, decision trees, and random forests
- Build these models from scratch and then with scikit-learn
Day 2: A Modern Machine Learning Workflow in Scikit-Learn
- Create a modern and comprehensive workflow for building an end-to-end machine learning solution
- Properly evaluate your model with a variety of different scoring metrics
- Select the best model through hyperparameter grid searching
- Impute missing values, transform your data, and engineer new features
- Use newly released tools to integrate pandas with scikit-learn
- Build a complex pipeline to contain the entire workflow
- Persist models onto disk so that they may be used later with new data
Major Course Objective
The major course objective is to learn the concepts and tools of an entire machine learning workflow so that you can apply it to any future problem. We will be using the scikit-learn library, an excellent tool for doing machine learning in python.
This course is intended for those who are getting started with the python data science ecosystem. A long precourse assignment is available to help introduce the fundamentals of python and pandas.
Refunds are available up to 30 days prior to the start of the event