Remove most qiskit.org links (#763) (#765)

* Remove most qiskit.org links

* Fix copyright

(cherry picked from commit 8df0fdf)

Co-authored-by: Eric Arellano <[email protected]>

CONTRIBUTING.md

@@ -5,7 +5,7 @@
 First please read the overall project contributing guidelines. These are
 included in the Qiskit documentation here:
 
-https://qiskit.org/documentation/contributing_to_qiskit.html
+https://github.com/Qiskit/qiskit/blob/main/CONTRIBUTING.md
 
 ## Contributing to Qiskit Machine Learning
 
@@ -61,8 +61,7 @@ please ensure that:
    to see the changes formatted output is as expected. You will find an index.html
    file in docs\_build\html and you can navigate from there.
 
-   Please note that a spell check is run in CI, on the docstrings, since the text
-   becomes part of the online [API Documentation](https://qiskit.org/documentation/).
+   Please note that a spell check is run in CI, on the docstrings.
 
    You can run `make spell` locally to check spelling though you would need to
    [install pyenchant](https://pyenchant.github.io/pyenchant/install.html) and be using

README.md

@@ -24,11 +24,11 @@ quantum neural networks. Two core implementations are readily provided, such as
 [EstimatorQNN](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html),
 and the [SamplerQNN](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html).
 The [EstimatorQNN](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.EstimatorQNN.html)
-leverages the [Estimator](https://qiskit.org/documentation/stubs/qiskit.primitives.BaseEstimator.html) primitive from Qiskit and 
+leverages the [Estimator](https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseEstimator) primitive from Qiskit and 
 allows users to combine parametrized quantum circuits with quantum mechanical observables. The circuits can be constructed using, for example, building blocks
 from Qiskit’s circuit library, and the QNN’s output is given by the expected value of the observable.
 The [SamplerQNN](https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.neural_networks.SamplerQNN.html)
-leverages another primitive introduced in Qiskit, the [Sampler](https://qiskit.org/documentation/stubs/qiskit.primitives.BaseSampler.html) primitive. 
+leverages another primitive introduced in Qiskit, the [Sampler](https://docs.quantum.ibm.com/api/qiskit/qiskit.primitives.BaseSampler) primitive. 
 This neural network translates quasi-probabilities of bitstrings estimated by the primitive into a desired output. This 
 translation step can be used to interpret a given bitstring in a particular context, e.g. translating it into a set of classes.
 
@@ -132,14 +132,13 @@ Learning path notebooks may be found in the
 of the documentation and are a great place to start. 
 
 Another good place to learn the fundamentals of quantum machine learning is the
-[Quantum Machine Learning](https://learn.qiskit.org/course/machine-learning/introduction) course 
-on the Qiskit Textbook's website. The course is very convenient for beginners who are eager to learn 
+[Quantum Machine Learning](https://github.com/Qiskit/textbook/tree/main/notebooks/quantum-machine-learning#) notebooks from the original Qiskit Textbook. The notebooks are convenient for beginners who are eager to learn 
 quantum machine learning from scratch, as well as understand the background and theory behind algorithms in
-Qiskit Machine Learning. The course covers a variety of topics to build understanding of parameterized
+Qiskit Machine Learning. The notebooks cover a variety of topics to build understanding of parameterized
 circuits, data encoding, variational algorithms etc., and in the end the ultimate goal of machine
 learning - how to build and train quantum ML models for supervised and unsupervised learning. 
-The textbook course is complementary to the tutorials of this module, where the tutorials focus
-on actual Qiskit Machine Learning algorithms, the course more explains and details underlying fundamentals
+The Textbook notebooks are complementary to the tutorials of this module; whereas these tutorials focus
+on actual Qiskit Machine Learning algorithms, the Textbook notebooks more explain and detail underlying fundamentals
 of quantum machine learning.
 
 ----------------------------------------------------------------------------------------------------

docs/getting_started.rst

@@ -8,7 +8,7 @@ Installation
 ============
 
 Qiskit Machine Learning depends on Qiskit, which has its own
-`Qiskit Getting Started <https://qiskit.org/documentation/getting_started.html>`__ detailing
+`installation instructions <https://docs.quantum.ibm.com/start/install>`__ detailing
 installation options and its supported environments/platforms. You should refer to
 that first. Then the information here can be followed which focuses on the additional installation
 specific to Qiskit Machine Learning.
@@ -21,10 +21,9 @@ See :ref:`optional_installs` for more information.
 
     .. tab-item:: Start locally
 
-        The simplest way to get started is to first follow the `getting started 'Start locally' guide for
-        Qiskit <https://qiskit.org/documentation/getting_started.html>`__
+        The simplest way to get started is to follow the installation guide for Qiskit `here <https://docs.quantum.ibm.com/start/install>`__
 
-        In your virtual environment, where you installed Qiskit, install Qiskit Machine Learning as follows:
+        In your virtual environment, where you installed Qiskit, install ``qiskit-machine-learning`` as follows:
 
         .. code:: sh
 
@@ -45,7 +44,7 @@ See :ref:`optional_installs` for more information.
 
        Since Qiskit Machine Learning depends on Qiskit, and its latest changes may require new or changed
        features of Qiskit, you should first follow Qiskit's `"Install from source"` instructions
-       here `Qiskit Getting Started <https://qiskit.org/documentation/getting_started.html>`__
+       `here <https://docs.quantum.ibm.com/start/install-qiskit-source>`__
 
        .. raw:: html