End-to-end Machine Learning: Time-series analysis
What you will learn
Build a weather predictor using python.
Use autocorrelation to build time-series features.
Detect and remove seasonal trends.
Handle missing values.
Download and ingest csv-formatted data.
Handle dates in with a custom python converter.
Evaluate a time-series model's performance.
Section 1: Introduction
Section 2: Get your data
Section 3: Find your features
Section 4: Build your model
Section 5: Deploy your model
Section 6: Wrap up
Build a weather predictor using python
- Some experience with python is helpful, but not required.
In this course, we'll walk through every step of making your own weather predictor. We'll find weather data, explore it and get it in order. We'll use the modeling tools of deseasonalization and linear regression to predict temperatures at the beach. We'll use the statistical tools of autoregression and confidence intervals to guide our feature selection and apply our results. And we'll code the whole thing up from scratch in python and organize it to be easy to read and easy to extend.
When you're done, you'll have a standalone weather predictor that can estimate high temperatures three days from now. You'll also have hands-on experience solving a real word data science problem from end to end.
If you are a professor or a teacher at any level, you are welcome to evaluate the course for free, and I can set your students up with a deep educational discount. Just contact me for the coupon code ([email protected]).
Who this course is for:
- Machine learning students and data scientists seeking project-based time series modeling and autocorrelation instruction.