End-to-end Machine Learning: Time-series analysis

End-to-end Machine Learning: Time-series analysis

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Course Description

Build a weather predictor using python

Requirements

  • Some experience with python is helpful, but not required.

Description

Welcome!

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 da

ys 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.

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.

Curriculum

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