Changes

Author: Krzysztof Joachimiak

README.md

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 * [TF-Agents](https://github.com/tensorflow/agents) - A library for Reinforcement Learning in TensorFlow. <img height="20" src="img/tf_big2.png" alt="sklearn"> 
 * [TensorForce](https://github.com/reinforceio/tensorforce) - A TensorFlow library for applied reinforcement learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
 * [TRFL](https://github.com/deepmind/trfl) - TensorFlow Reinforcement Learning. <img height="20" src="img/tf_big2.png" alt="sklearn">
-* [Dopamine](https://github.com/google/dopamine) - A research framework for fast prototyping of reinforcement learning algorithms. 
+* [Dopamine](https://github.com/google/dopamine) - A research framework for fast prototyping of reinforcement learning algorithms.
 * [keras-rl](https://github.com/keras-rl/keras-rl) - Deep Reinforcement Learning for Keras. <img height="20" src="img/keras_big.png" alt="Keras compatible">
 * [ChainerRL](https://github.com/chainer/chainerrl) - A deep reinforcement learning library built on top of Chainer.
 
@@ -412,7 +412,7 @@
 * [Pandas Profiling](https://github.com/pandas-profiling/pandas-profiling) - Create HTML profiling reports from pandas DataFrame objects. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
 * [statsmodels](https://github.com/statsmodels/statsmodels) - Statistical modeling and econometrics in Python.
 * [stockstats](https://github.com/jealous/stockstats) - Supply a wrapper ``StockDataFrame`` based on the ``pandas.DataFrame`` with inline stock statistics/indicators support.
-* [weightedcalcs](https://github.com/jsvine/weightedcalcs) - pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.
+* [weightedcalcs](https://github.com/jsvine/weightedcalcs) - A pandas-based utility to calculate weighted means, medians, distributions, standard deviations, and more.
 * [scikit-posthocs](https://github.com/maximtrp/scikit-posthocs) - Pairwise Multiple Comparisons Post-hoc Tests.
 * [Alphalens](https://github.com/quantopian/alphalens) - Performance analysis of predictive (alpha) stock factors.
 
@@ -423,7 +423,7 @@
 * [Veles](https://github.com/Samsung/veles) - Distributed machine learning platform.
 * [Jubatus](https://github.com/jubatus/jubatus) - Framework and Library for Distributed Online Machine Learning.
 * [DMTK](https://github.com/Microsoft/DMTK) - Microsoft Distributed Machine Learning Toolkit.
-* [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) - PArallel Distributed Deep LEarning
+* [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) - PArallel Distributed Deep LEarning.
 * [dask-ml](https://github.com/dask/dask-ml) - Distributed and parallel machine learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [Distributed](https://github.com/dask/distributed) - Distributed computation in Python.
 
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 ## Evaluation
 * [recmetrics](https://github.com/statisticianinstilettos/recmetrics) - Library of useful metrics and plots for evaluating recommender systems.
 * [Metrics](https://github.com/benhamner/Metrics) - Machine learning evaluation metric.
-* [sklearn-evaluation](https://github.com/edublancas/sklearn-evaluation) - scikit-learn model evaluation made easy: plots, tables and markdown reports.
+* [sklearn-evaluation](https://github.com/edublancas/sklearn-evaluation) - Model evaluation made easy: plots, tables and markdown reports. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 * [AI Fairness 360](https://github.com/IBM/AIF360) - Fairness metrics for datasets and ML models, explanations and algorithms to mitigate bias in datasets and models.
 
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