211. Decision Trees with Python and Pandas
Time your subway commute
In this course, we'll build and use decision trees, a popular and versatile tool that will serve you well in your applied machine learning work.
The data science problem we want to solve is predicting transit times on a public transportation system. We will walk through the entire process from end to end:
- Define the problem
- Gather the data
- Clean and prepare the data
- Build a decision tree
- Train the model
- Generate predictions
- Wrap the model in some code that makes it easy to use
If you are a professor or a teacher at any level, email me ([email protected]) and I'll set you up to evaluate the course for free.I hope you enjoy the process of building a complete solution to a data science problem from the ground up.
"Very enjoyable, personable instruction. A lot more detailed, esp building a decision tree from absolute scratch rather than just another use of the library.... " - J. Bater
"Thank you for making this course! I really enjoy the end-to-end aspect of it. As well, I appreciate your advice to build a parsimonious model. I love all the critical tips on building and evaluating decision trees. " - R. Low
"Great content. I am going to recommend this course to all of my friends. " - S. Gairola
I love solving puzzles and building things. Machine learning lets me do both. I got started by studying robotics and human rehabilitation at MIT (MS '99, PhD '02), moved on to machine vision and machine learning at Sandia National Laboratories, then to predictive modeling of agriculture DuPont Pioneer, and cloud data science at Microsoft. At Facebook I worked to get internet and electrical power to those in the world who don't have it, using deep learning and satellite imagery and to do a better job identifying topics reliably in unstructured text. Now at iRobot I work to help robots get better and better at doing their jobs. In my spare time I like to rock climb, write robot learning algorithms, and go on walks with my wife and our dog, Reign of Terror.