Explanation
Engineering Techniques for Time Series Forecasting is the most comprehensive online course on engineering techniques for time series forecasting. In this course, you will learn various engineering methods to generate and generate time series profiles to generate profiles suitable for use in off-the-shelf regression models such as – linear regression, random forest, and gradient boosting machines.
What you will learn in this course:
- How to use traditional machine learning models
- How to account for missing data in time series forecasting
- How to create front-end windows data views and delays
- How to code different variables for time series forecasting
- Projecting several steps into the future (several steps forward instead of just one step forward)
- How to transform a time series into a table of predictable features
- How to identify and eliminate outliers in the forecast period
- and many more
Who is this course for:
- For those who want to continue the data collection in the time series
- Data scientists who want to learn engineering techniques for time series forecasting
- Data scientists who want to improve their coding skills in feature engineering
- Data scientists who want to learn more about feature engineering
Course specifications:
- Publisher: Udemy
- Teacher: Soledad Galli , Kishan Manani
- Language : English
- Level: intermediate
- Duration: 18h 9m
- number of lessons: 142
- Format: mp4
The course includes:
Requirements:
- Installing Python
- Installing the Jupyter notebook
- Python coding skills
- Some experience with Numpy, Pandas and Matplotlib
- Knowledge of Scikit-Learn
- Knowledge of machine learning algorithms
Technical Illustrations for Time Series Forecasting
Example clip:
Installation guide:
After the production, you can watch the course in your favorite video.
subtitle: None
Quality: 720p
Download links:
Password: free download software
File size:
5.11 GB