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Create a single tutorial showing how one could use concrete-ml for LinearSVC.

The tutorials need to be very educative, pedagogical and help the user understand how Concrete-ML is to be used. The tutorial(s) must use Virtual Library and FHE computations. It is even better if tutorials use a real-life kind of dataset. We want the tutorials to be as complete and to have as much quality as Linear Regression tutorial or the Poisson Regression tutorial.

Writing the tutorial should be done following 3 steps, each being peer reviewed along the way:

• Choose a dataset and analyse it in cleartext using scikit-learn
• Analyse the data in cyphertext using concrete-ml and compare results against cleartext inference
• Assemble the tutorial with introduction, sections, discussions, conclusion

Create a single tutorial showing how one could use concrete-ml for a LinearSVR model.

The tutorials need to be very educative, pedagogical and help the user understand how Concrete-ML is to be used. The tutorial(s) must use Virtual Library and FHE computations. It is even better if tutorials use a real-life kind of dataset. We want the tutorials to be as complete and to have as much quality as Linear Regression tutorial or the Poisson Regression tutorial.

Writing the tutorial should be done following 3 steps, each being peer reviewed along the way:

• Choose a dataset and analyse it in cleartext using scikit-learn
• Analyse the data in cyphertext using concrete-ml and compare results against cleartext inference
• Assemble the tutorial with introduction, sections, discussions, conclusion

Create a single tutorial showing how one could use concrete-ml for penalised linear regression, including Lasso, Ridge and ElasticNet.

The tutorials need to be very educative, pedagogical and help the user understand how Concrete-ML is to be used. The tutorial(s) must use Virtual Library and FHE computations. It is even better if tutorials use a real-life kind of dataset. We want the tutorials to be as complete and to have as much quality as Linear Regression tutorial or the Poisson Regression tutorial.

Writing the tutorial should be done following 3 steps, each being peer reviewed along the way:

• Choose a dataset and analyse it in cleartext using scikit-learn
• Analyse the data in cyphertext using concrete-ml and compare results against cleartext inference
• Assemble the tutorial with introduction, sections, discussions, conclusion

Create a single tutorial showing how one could use concrete-ml for a DecisionTreeRegressor.

The tutorials need to be very educative, pedagogical and help the user understand how Concrete-ML is to be used. The tutorial(s) must use Virtual Library and FHE computations. It is even better if tutorials use a real-life kind of dataset. We want the tutorials to be as complete and to have as much quality as Linear Regression tutorial or the Poisson Regression tutorial.

Writing the tutorial should be done following 3 steps, each being peer reviewed along the way:

• Choose a dataset and analyse it in cleartext using scikit-learn
• Analyse the data in cyphertext using concrete-ml and compare results against cleartext inference
• Assemble the tutorial with introduction, sections, discussions, conclusion