Update README.md

Author: khuyentran1401

README.md

@@ -19,6 +19,7 @@
 ## Contents
 * [Machine Learning](#machine-learning)
 * [Deep Learning](#deep-learning)
+* [Web Scraping](#web-scraping)
 * [Data Manipulation](#data-manipulation)
 * [Feature Engineering](#feature-engineering)
 * [Visualization](#visualization)
@@ -186,6 +187,12 @@
 * [Caffe2](https://github.com/pytorch/pytorch/tree/master/caffe2) - A lightweight, modular, and scalable deep learning framework (now a part of PyTorch).
 * [hipCaffe](https://github.com/ROCmSoftwarePlatform/hipCaffe) - The HIP port of Caffe. <img height="20" src="img/amd_big.png" alt="Possible to run on AMD GPU">
 
+## Web Scraping
+* [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/): The easiest library to scrape static websites for beginners
+* [Scrapy](https://scrapy.org/): Fast and extensible scraping library. Can write rules and create customized scraper without touching the coure
+* [Selenium](https://selenium-python.readthedocs.io/installation.html#introduction): Use Selenium Python API to access all functionalities of Selenium WebDriver in an intuitive way like a real user.
+* [Pattern](https://github.com/clips/pattern): High level scraping for well-establish websites such as Google, Twitter, and Wikipedia. Also has NLP, machine learning algorithms, and visualization 
+
 ## Data Manipulation
 
 ### Data Containers
@@ -216,6 +223,7 @@
 * [meza](https://github.com/reubano/meza) - A Python toolkit for processing tabular data.
 * [Prodmodel](https://github.com/prodmodel/prodmodel) - Build system for data science pipelines.
 * [dopanda](https://github.com/dovpanda-dev/dovpanda) - Hints and tips for using pandas in an analysis environment. <img height="20" src="img/pandas_big.png" alt="pandas compatible">
+* [CircleCi](https://circleci.com/): Automates your software builds, tests, and deployments.
 
 ## Feature Engineering
 
@@ -234,19 +242,26 @@
 * [scikit-rebate](https://github.com/EpistasisLab/scikit-rebate) - A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning. <img height="20" src="img/sklearn_big.png" alt="sklearn">
 
 ## Visualization
+### General Purposes
 * [Matplotlib](https://github.com/matplotlib/matplotlib) - Plotting with Python.
 * [seaborn](https://github.com/mwaskom/seaborn) - Statistical data visualization using matplotlib.
-* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.
-* [HoloViews](https://github.com/ioam/holoviews) - Stop plotting your data - annotate your data and let it visualize itself.
 * [prettyplotlib](https://github.com/olgabot/prettyplotlib) - Painlessly create beautiful matplotlib plots.
 * [python-ternary](https://github.com/marcharper/python-ternary) - Ternary plotting library for python with matplotlib.
 * [missingno](https://github.com/ResidentMario/missingno) - Missing data visualization module for Python.
 * [chartify](https://github.com/spotify/chartify/) - Python library that makes it easy for data scientists to create charts.
 * [physt](https://github.com/janpipek/physt) - Improved histograms.
+### Interactive plots
 * [animatplot](https://github.com/t-makaro/animatplot) - A python package for animating plots build on matplotlib.
 * [plotly](https://plot.ly/python/) - A Python library that makes interactive and publication-quality graphs.
+* [Bokeh](https://github.com/bokeh/bokeh) - Interactive Web Plotting for Python.
+* [Altair](https://altair-viz.github.io/): Declarative statistical visualization library for Python. Can easily do many data transformation within the code to create graph
+### Map
 * [folium](https://python-visualization.github.io/folium/quickstart.html#Getting-Started) - Makes it easy to visualize data on an interactive open street map
 * [geemap](https://github.com/giswqs/geemap) - Python package for interactive mapping with Google Earth Engine (GEE)
+### Automatic Plotting
+* [HoloViews](https://github.com/ioam/holoviews) - Stop plotting your data - annotate your data and let it visualize itself.
+* [AutoViz](https://github.com/AutoViML/AutoViz): Visualize data automatically with 1 line of code
+
 
 
 ## Deployment