Updated README for ch1-3

Author: raghavbali

notebooks/Ch01_Machine_Learning_Basics/README.md

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 # Chapter 1: Machine Learning Basics
 
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+This chapter familiarizes and acquaints readers with the basics of machine learning, industry standard workflows followed for machine learning processes and expands on the different types of machine learning and deep learning algorithms. We also present different applications of machine learning.

notebooks/Ch02_The_Python_ML_Ecosystem/README.md

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 <p align="right"><a target="_blank" href="https://github.com/dipanjanS/practical-machine-learning-with-python#contents"><img height="50px" src="https://media.githubusercontent.com/media/dipanjanS/practical-machine-learning-with-python/master/media/assets/home_page.png" /></a><a target="_blank" href="https://github.com/dipanjanS/practical-machine-learning-with-python/tree/master/notebooks#chapter-2-the-python-machine-learning-ecosystem"><img height="50px" src="https://media.githubusercontent.com/media/dipanjanS/practical-machine-learning-with-python/master/media/assets/contents_page.jpg" /></a><a target="_blank" href="https://github.com/dipanjanS/practical-machine-learning-with-python/tree/master/notebooks/Ch01_Machine_Learning_Basics#chapter-1-machine-learning-basics"><img height="50px" src="https://media.githubusercontent.com/media/dipanjanS/practical-machine-learning-with-python/master/media/assets/back_page.png" /></a></a><a target="_blank" href="https://github.com/dipanjanS/practical-machine-learning-with-python/tree/master/notebooks/Ch03_Processing_Wrangling_and_Visualizing_Data#chapter-3-processing-wrangling-and-visualizing-data"><img height="50px" src="https://media.githubusercontent.com/media/dipanjanS/practical-machine-learning-with-python/master/media/assets/next_page.png" /></a></p>
 
 # Chapter 2: The Python Machine Learning Ecosystem
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+This chapter introduces readers to the python language and the entire ecosystem built around machine learning with python tools, frameworks and libraries. Overview and code samples are given for each tool to depict its usage and effectiveness. This chapter introduces tools/frameworks such as Anaconda distribution, pandas, numpy, scipy, sklearn, theano, tensorflow, keras and many more

notebooks/Ch03_Processing_Wrangling_and_Visualizing_Data/README.md

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 <p align="right"><a target="_blank" href="https://github.com/dipanjanS/practical-machine-learning-with-python#contents"><img height="50px" src="https://media.githubusercontent.com/media/dipanjanS/practical-machine-learning-with-python/master/media/assets/home_page.png" /></a><a target="_blank" href="https://github.com/dipanjanS/practical-machine-learning-with-python/tree/master/notebooks#chapter-3-processing-wrangling-and-visualizing-data"><img height="50px" src="https://media.githubusercontent.com/media/dipanjanS/practical-machine-learning-with-python/master/media/assets/contents_page.jpg" /></a><a target="_blank" href="https://github.com/dipanjanS/practical-machine-learning-with-python/tree/master/notebooks/Ch02_The_Python_ML_Ecosystem#chapter-2-the-python-machine-learning-ecosystem"><img height="50px" src="https://media.githubusercontent.com/media/dipanjanS/practical-machine-learning-with-python/master/media/assets/back_page.png" /></a></a><a target="_blank" href="https://github.com/dipanjanS/practical-machine-learning-with-python/tree/master/notebooks/Ch04_Feature_Engineering_and_Selection#chapter-4-feature-engineering-and-selection"><img height="50px" src="https://media.githubusercontent.com/media/dipanjanS/practical-machine-learning-with-python/master/media/assets/next_page.png" /></a></p>
 
 # Chapter 3: Processing, Wrangling and Visualizing Data
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+This chapter focuses on data retrieval, processing, wrangling and visualizations. Hands-on examples with various data formats have been depicted along with  ways of leveraging various frameworks for handling data & summarization. This chapter also covers aspects relation to Visualization of data to get insights.