Here's a paraphrase of the summaries for the tasks:
**Extracting data from images (Task 1a):**
* You'll need to add a new function to existing code that can grab specific values from images stored on Google Earth Engine (GEE).
* These values, representing features of the land, will be used to train a machine learning model later.
**Creating a new feature (Task 1b):**
* You'll write some new code to define a new feature based on existing data (called NDVI).
* This involves creating a new class that inherits from existing ones to handle this specific feature extraction.
**Training machine learning models (Task 2a):**
* Using all the features you extracted in Task 1, you'll train different machine learning models for a client.
* The client has collected soil samples, and you can potentially combine their data with yours to improve the model's accuracy for their specific property.
**Evaluating model accuracy (Task 2b):**
* You'll need to assess how well the models you trained in Task 2a perform on the client's data.
* This involves choosing appropriate metrics to measure accuracy and comparing the different approaches you used.
* Based on the results, you'll decide which model performed best for the client's property.
* This information will be crucial when communicating with the client in the next task.