feature-engineering-tutorials
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feature-engineering-tutorials
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How to balance multiple time series data?
I’ve actually solved a similar problem several times in a variety of settings. I’ve had success with boosted trees and feature engineering on the sensor readings over time. I treat each reading as an observation and set the target to be the value I want to forecast (e.g. one hour ahead, the sum over the next day, the value at the same time the next day). There was a recent paper that compared boosted trees to deep learning techniques and found the boosted trees performed really well. Next, I perform feature engineering to aggregate the data up to the current time. These features will include the current value, lagged values over multiple observations for that sensor, more complicated features from moving statistics over different time scales, etc. I actually wrote a blog about creating these features using the open-source package RasgoQL and have similar types of features shared in the open-source repository here. I have also had success creating these sorts of historical features using the tsfresh package. Finally, when evaluating the forecast, use a time based split so earlier data is used to train the model and later data to evaluate the model.
MachineLearningNotebooks
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Multiple model loading on a Online Fully managed endpoint
I found an example using the python SDK v2:
- I Took The Azure DP-100 exam today and passed it
What are some alternatives?
jupyter-notebook-chatcompletion - Jupyter Notebook ChatCompletion is VSCode extension that brings the power of OpenAI's ChatCompletion API to your Jupyter Notebooks!
azureml-examples - Official community-driven Azure Machine Learning examples, tested with GitHub Actions.
intro-to-python - [READ-ONLY MIRROR] An intro to Python & programming for wanna-be data scientists
One-Piece-Image-Classifier - A quick image classifier trained with manually selected One Piece images.
dtreeviz - A python library for decision tree visualization and model interpretation.
mlops-v2 - Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
ydata-quality - Data Quality assessment with one line of code
computervision-recipes - Best Practices, code samples, and documentation for Computer Vision.
gastrodon - Visualize RDF data in Jupyter with Pandas
MLOps - MLOps examples
PRML - PRML algorithms implemented in Python
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.