Course description
Feature Engineering for Time Series Forecasting is the most complete online course on feature engineering for time series forecasting. In this course, you will learn different feature engineering methods for extracting and creating features from time series so that you can extract features suitable for use in off-the-shelf regression models such as linear regression, random forest, and gradient boosting machines. be
What you will learn in this course:
- How to learn using traditional machine learning models
- How to impute missing data for time series forecasting
- How to create features from past data through windows and delays
- How to code classified variables for predicting time series
- Predicting several steps into the future (several steps ahead instead of just one step ahead)
- How to transform time series into a table of predictable features
- How to identify and remove outliers in time series forecasting
- And …
Who is this course suitable for?
- Those who want to pre-process datasets for time series
- Data scientists who want to learn feature engineering techniques for time series forecasting
- Data scientists who want to improve their coding skills for feature engineering
- Data scientists who want to learn more techniques for feature engineering
Course specifications :
- Publisher: Udemy
- teacher : Soledad Galli And Kishan Manani
- English language
- Education level: regular
- Duration: 18 hours and 9 minutes
- Number of courses: 142
- File format: mp4
Course headings :
Course prerequisites:
- A Python installation
- Jupyter notebook installation
- Python coding skills
- Some experience with Numpy, Pandas and Matplotlib
- Familiarity with Scikit-Learn
- Familiarity with machine learning algorithms
Images of Feature Engineering for Time Series Forecasting
Sample video
Installation guide
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Quality: 720p
download link
Password file(s): www.downloadly.ir
Size
5.11 GB
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