Update README.md

Author: vijay-arya

README.md

@@ -8,7 +8,7 @@ The AI Explainability 360 toolkit is an open-source library that supports interp
 
 The [AI Explainability 360 interactive experience](https://aix360.res.ibm.com/) provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The [tutorials and example notebooks](./examples) offer a deeper, data scientist-oriented introduction. The complete API is also available. 
 
-There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. To help, we have created some [guidance material](http://aix360.mybluemix.net/resources#guidance) and a [taxonomy tree](./aix360/algorithms/README.md) that can be consulted. 
+There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. To help, we have created some [guidance material](https://aix360.res.ibm.com/resources#guidance) and a [taxonomy tree](./aix360/algorithms/README.md) that can be consulted. 
 
 We have developed the package with extensibility in mind. This library is still in development. We encourage you to contribute your explainability algorithms, metrics, and use cases. To get started as a contributor, please join the [AI Explainability 360 Community on Slack](https://aix360.slack.com) by requesting an invitation [here](https://join.slack.com/t/aix360/shared_invite/enQtNzEyOTAwOTk1NzY2LTM1ZTMwM2M4OWQzNjhmNGRiZjg3MmJiYTAzNDU1MTRiYTIyMjFhZTI4ZDUwM2M1MGYyODkwNzQ2OWQzMThlN2Q). Please review the instructions to contribute code and python notebooks [here](CONTRIBUTING.md).