Merge pull request #1170 from GeoscienceAustralia/feature/fixing-link…

…s-jan-2024

Fixed only four links

DEA_products/DEA_Wetlands_Insight_Tool.ipynb

@@ -70,7 +70,7 @@
     "\n",
     "## Related products\n",
     "* [DEA Fractional Cover (Landsat)](../DEA_products/DEA_Fractional_Cover.ipynb)\n",
-    "* [DEA Tasseled Cap Indices Percentiles Calendar Year (Landsat)](https://docs.dea.ga.gov.au/data/product/dea-wetness-percentiles-landsat)\n",
+    "* [DEA Tasseled Cap Indices Percentiles Calendar Year (Landsat)](https://docs.dea.ga.gov.au/data/product/dea-tasseled-cap-percentiles-landsat/)\n",
     "* [DEA Water Observations (Landsat)](../DEA_products/DEA_Water_Observations.ipynb)"
    ]
   },

Real_world_examples/Scalable_machine_learning/0_README.ipynb

@@ -49,7 +49,7 @@
     "* There are many online courses that can help you understand the fundamentals of machine learning with python e.g. [edX](https://www.edx.org/course/machine-learning-with-python-a-practical-introduct), [coursera](https://www.coursera.org/learn/machine-learning-with-python). \n",
     "* The [Scikit-learn](https://scikit-learn.org/stable/supervised_learning.html) documentation provides information on the available models and their parameters.\n",
     "* This [review article](https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1433343) provides a nice overview of machine learning in the context of remote sensing.\n",
-    "* The stand alone notebook, [Machine_learning_with_ODC](../How_to_guides/Machine_learning_with_ODC.ipynb), in the `Real_world_examples/` folder is a companion piece to these notebooks and provides a more succint (but less descriptive) version of the workflow demonstrated here.\n",
+    "* The stand alone notebook, [Machine_learning_with_ODC](../../How_to_guides/Machine_learning_with_ODC.ipynb), in the `Real_world_examples/` folder is a companion piece to these notebooks and provides a more succint (but less descriptive) version of the workflow demonstrated here.\n",
     "___\n"
    ]
   },

Real_world_examples/Scalable_machine_learning/4_Classify_satellite_data.ipynb

@@ -289,9 +289,9 @@
    "source": [
     "### Loop through test locations and predict\n",
     "\n",
-    "For every location we listed in the `test_locations` dictionary, we calculate the feature layers, and then use the DEA function [predict_xr](../Tools/gen/dea_tools.classification.ipynb#L231) to classify the data.\n",
+    "For every location we listed in the `test_locations` dictionary, we calculate the feature layers, and then use the DEA function [predict_xr](../Tools/gen/dea_tools.classification/#dea_tools.classification.predict_xr) to classify the data.\n",
     "\n",
-    "The `predict_xr` function is an xarray wrapper around the sklearn estimator `.predict()` and `.predict_proba()` methods, and relies on [dask-ml](https://ml.dask.org/) [ParallelPostfit](https://ml.dask.org/modules/generated/dask_ml.wrappers.ParallelPostFit.html) to run the predictions with dask. `Predict_xr` can compute predictions, prediction probabilites, and return the input feature layers. Read the [documentation](../Tools/gen/dea_tools.classification.ipynb#L239) for more insights into this function's capabilities."
+    "The `predict_xr` function is an xarray wrapper around the sklearn estimator `.predict()` and `.predict_proba()` methods, and relies on [dask-ml](https://ml.dask.org/) [ParallelPostfit](https://ml.dask.org/modules/generated/dask_ml.wrappers.ParallelPostFit.html) to run the predictions with dask. `Predict_xr` can compute predictions, prediction probabilites, and return the input feature layers. Read the [documentation](../Tools/gen/dea_tools.classification/#dea_tools.classification.predict_xr) for more insights into this function's capabilities."
    ]
   },
   {