Update README.md

README.md

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 Please consider citing the [**JMLR-MLOSS Manuscript**](http://jmlr.org/papers/volume18/17-636/17-636.pdf) if you've used pomegranate in your academic work!
 
-pomegranate is a package for probabilistic models in Python that is implemented in cython for speed. It's focus is on merging the easy-to-use scikit-learn API with the modularity that comes with probabilistic modeling to allow users to specify complicated models without needing to worry about implementation details. The models are built from the ground up with big data processing in mind and so natively support features like out-of-core learning and parallelism. Click on the binder badge above to interactively play with the tutorials!
+pomegranate is a package for building probabilistic models in Python that is implemented in Cython for speed. A primary focus of pomegranate is to merge the easy-to-use API of scikit-learn with the modularity of probabilistic modeling to allow users to specify complicated models without needing to worry about implementation details. The models implemented here are built from the ground up with big data processing in mind and so natively support features like multi-threaded parallelism and out-of-core processing. Click on the binder badge above to interactively play with the tutorials!
 
 ### Installation
 
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 * [Naive Bayes and Bayes Classifiers](http://pomegranate.readthedocs.io/en/latest/NaiveBayes.html)
 * [Markov Chains](http://pomegranate.readthedocs.io/en/latest/MarkovChain.html)
 * [Discrete Bayesian Networks](http://pomegranate.readthedocs.io/en/latest/BayesianNetwork.html)
+* [Discrete Markov Networks](https://pomegranate.readthedocs.io/en/latest/MarkovNetwork.html)
 
 The discrete Bayesian networks also support novel work on structure learning in the presence of constraints through a constraint graph. These constraints can dramatically speed up structure learning through the use of loose general prior knowledge, and can frequently make the exact learning task take only polynomial time instead of exponential time. See the [PeerJ manuscript](https://peerj.com/articles/cs-122/) for the theory and the [pomegranate tutorial](https://github.com/jmschrei/pomegranate/blob/master/tutorials/B_Model_Tutorial_4b_Bayesian_Network_Structure_Learning.ipynb) for the practical usage! 
 
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 ### Features
 
+* [sklearn-like API](https://pomegranate.readthedocs.io/en/latest/api.html)
 * [Multi-threaded Training](http://pomegranate.readthedocs.io/en/latest/parallelism.html)
 * [BLAS/GPU Acceleration](http://pomegranate.readthedocs.io/en/latest/gpu.html)
 * [Out-of-Core Learning](http://pomegranate.readthedocs.io/en/latest/ooc.html)
-* [Minibatch Learning](http://pomegranate.readthedocs.io/en/latest/minibatch.html)
+* [Data Generators and IO](https://pomegranate.readthedocs.io/en/latest/io.html)
 * [Semi-supervised Learning](http://pomegranate.readthedocs.io/en/latest/semisupervised.html)
 * [Missing Value Support](http://pomegranate.readthedocs.io/en/latest/nan.html)
 * [Customized Callbacks](http://pomegranate.readthedocs.io/en/latest/callbacks.html)