Update README.md

Author: r0f1

README.md

@@ -6,8 +6,7 @@
 [pandas](https://pandas.pydata.org/) - Data structures built on top of [numpy](https://www.numpy.org/).  
 [scikit-learn](https://scikit-learn.org/stable/) - Core ML library.  
 [matplotlib](https://matplotlib.org/) - Plotting library.  
-[animatplot](https://github.com/t-makaro/animatplot) - Animate plots build on matplotlib.  
-[seaborn](https://seaborn.pydata.org/) - Python data visualization library based on matplotlib.  
+[seaborn](https://seaborn.pydata.org/) - Data visualization library based on matplotlib.  
 [pandas_summary](https://github.com/mouradmourafiq/pandas-summary) - Basic statistics using `DataFrameSummary(df).summary()`.  
 [pandas_profiling](https://github.com/pandas-profiling/pandas-profiling) - Descriptive statistics using `ProfileReport`.  
 [sklearn_pandas](https://github.com/scikit-learn-contrib/sklearn-pandas) - Helpful `DataFrameMapper` class.  
@@ -108,6 +107,7 @@ General ticks: [link](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks
 [mosaic plots](https://www.statsmodels.org/dev/generated/statsmodels.graphics.mosaicplot.mosaic.html) - Categorical variable visualization, [example](https://sukhbinder.wordpress.com/2018/09/18/mosaic-plot-in-python/).  
 [yellowbrick](https://github.com/DistrictDataLabs/yellowbrick) - Wrapper for matplotlib for diagnosic ML plots.  
 [bokeh](https://bokeh.pydata.org/en/latest/) - Interactive visualization library, [Examples](https://bokeh.pydata.org/en/latest/docs/user_guide/server.html), [Examples](https://github.com/WillKoehrsen/Bokeh-Python-Visualization).  
+[animatplot](https://github.com/t-makaro/animatplot) - Animate plots build on matplotlib.  
 [plotnine](https://github.com/has2k1/plotnine) - ggplot for Python.  
 [altair](https://altair-viz.github.io/) - Declarative statistical visualization library.  
 [bqplot](https://github.com/bloomberg/bqplot) - Plotting library for IPython/Jupyter Notebooks.  
@@ -146,7 +146,7 @@ Predict economic indicators from Open Street Map [ipynb](https://github.com/njan
 Examples: [1](https://lazyprogrammer.me/tutorial-on-collaborative-filtering-and-matrix-factorization-in-python/), [2](https://medium.com/@james_aka_yale/the-4-recommendation-engines-that-can-predict-your-movie-tastes-bbec857b8223), [2-ipynb](https://github.com/khanhnamle1994/movielens/blob/master/Content_Based_and_Collaborative_Filtering_Models.ipynb), [3](https://www.kaggle.com/morrisb/how-to-recommend-anything-deep-recommender).  
 [surprise](https://github.com/NicolasHug/Surprise) - Recommender, [talk](https://www.youtube.com/watch?v=d7iIb_XVkZs).  
 [turicreate](https://github.com/apple/turicreate) - Recommender.  
-[implicit](https://github.com/benfred/implicit) - Fast Python Collaborative Filtering for Implicit Feedback Datasets.  
+[implicit](https://github.com/benfred/implicit) - Fast Collaborative Filtering for Implicit Feedback Datasets.  
 [spotlight](https://github.com/maciejkula/spotlight) - Deep recommender models using PyTorch.  
 [lightfm](https://github.com/lyst/lightfm) - Recommendation algorithms for both implicit and explicit feedback.  
 [funk-svd](https://github.com/gbolmier/funk-svd) - Fast SVD.  
@@ -287,7 +287,7 @@ Understanding SVM Regression: [slides](https://cs.adelaide.edu.au/~chhshen/teach
 [Kalman Filter book](https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python) - Focuses on intuition using Jupyter Notebooks. Includes Baysian and various Kalman filters.  
 [Interactive Tool](https://fiiir.com/) for FIR and IIR filters, [Examples](https://plot.ly/python/fft-filters/).  
 [The Scientist & Engineer's Guide to Digital Signal Processing (1999)](https://www.analog.com/en/education/education-library/scientist_engineers_guide.html).  
-[filterpy](https://github.com/rlabbe/filterpy) - Python Kalman filtering and optimal estimation library.  
+[filterpy](https://github.com/rlabbe/filterpy) - Kalman filtering and optimal estimation library.  
 
 #### Time Series
 [statsmodels](https://www.statsmodels.org/dev/tsa.html) - Time series analysis, [seasonal decompose](https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.seasonal_decompose.html) [example](https://gist.github.com/balzer82/5cec6ad7adc1b550e7ee), [SARIMA](https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAX.html), [granger causality](http://www.statsmodels.org/dev/generated/statsmodels.tsa.stattools.grangercausalitytests.html).