Python is one of the most popular languages used in machine learning, data science, and predictive analytics. In this hands-on course, you will how to use Python, scikit-learn, and lightgbm to create regression and decision tree models. You will leave with complete code examples that you can use and build on in your own work.
You Will Learn
- What a classification model is and how to train two types (logistic regression and decision tree classifiers)
- What a regression model is and how to train two types (linear regression and neural networks)
- How to perform feature engineering and why it is important
- How to properly set up a training, cross-validation, and testing workflow
- How to guard against overfitting with regularization and hyper-parameters
- Individuals with basic Python experience who are new to using Python for data analysis
- Linux, OS X, or Windows
- 32- or 64-bit computer
- 8 MB available RAM
- 600 MB available hard drive space to download and install Anaconda
(We use Anaconda because it is a free, no-hassle way to install Python and the needed libraries. Instructions will be emailed to registrants prior to the event to prepare your laptop BEFORE the conference. There is no time allotted in class for laptop preparation.)
* Enrollment is limited to 40 attendees.