Updated index.rst

Author: tirthajyoti

docs/index.rst

@@ -1,211 +1,185 @@
-.. image:: https://readthedocs.org/projects/machine-learning-with-python/badge/?version=latest
-   :width: 15 %
-.. image:: https://img.shields.io/badge/License-BSD%202--Clause-orange.svg
-   :width: 22 %
-.. image:: https://img.shields.io/github/forks/tirthajyoti/Machine-Learning-with-Python.svg
-   :width: 13 %
-.. image:: https://img.shields.io/github/stars/tirthajyoti/Machine-Learning-with-Python.svg
-   :width: 13 %
-   
-|
-   
-Python Machine Learning Notebooks (Tutorial style)
-==================================================
-
-Dr. Tirthajyoti Sarkar, Fremont, CA (`Please feel free to add me on
-LinkedIn
-here <https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7>`__)
-
---------------
-
-Requirements
-===============
-
--  Python 3.5+
--  NumPy (``$ pip install numpy``)
--  Pandas (``$ pip install pandas``)
--  Scikit-learn (``$ pip install scikit-learn``)
--  SciPy (``$ pip install scipy``)
--  Statsmodels (``$ pip install statsmodels``)
--  MatplotLib (``$ pip install matplotlib``)
--  Seaborn (``$ pip install seaborn``)
--  Sympy (``$ pip install sympy``)
-
---------------
-
-You can start with this article that I wrote in Heartbeat magazine (on
-Medium platform): 
-
-`“Some Essential Hacks and Tricks for Machine Learning
-with
-Python” <https://heartbeat.fritz.ai/some-essential-hacks-and-tricks-for-machine-learning-with-python-5478bc6593f2>`__
-
-.. image:: https://cookieegroup.com/wp-content/uploads/2018/10/2-1.png"
-   :width: 500px
-   :align: center
-   :height: 350px
-   :alt: alternate text
-
-Essential tutorial-type notebooks on Pandas and Numpy
-=======================================================
-
-Jupyter notebooks covering a wide range of functions and operations on
-the topics of NumPy, Pandans, Seaborn, matplotlib etc.
-
--  `Basics of Numpy
-   array <https://github.com/tirthajyoti/PythonMachineLearning/blob/master/Pandas%20and%20Numpy/Basics%20of%20Numpy%20arrays.ipynb>`__
-
--  `Basics of Pandas
-   DataFrame <https://github.com/tirthajyoti/PythonMachineLearning/blob/master/Pandas%20and%20Numpy/Basics%20of%20Pandas%20DataFrame.ipynb>`__
-
--  `Basics of Matplotlib and Descriptive
-   Statistics <https://github.com/tirthajyoti/PythonMachineLearning/blob/master/Pandas%20and%20Numpy/Basics%20of%20Matplotlib%20and%20Descriptive%20Statistics.ipynb>`__
-
---------------
-
-Regression
-===============
-.. image:: https://slideplayer.com/slide/6053182/20/images/10/Simple+Linear+Regression+Model.jpg
-   :width: 400px
-   :align: center
-   :height: 300px
-   :alt: alternate text
-
--  Simple linear regression with t-statistic generation
-
--  `Multiple ways to perform linear regression in Python and their speed
-   comparison <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Linear_Regression_Methods.ipynb>`__
-   (`check the article I wrote on
-   freeCodeCamp <https://medium.freecodecamp.org/data-science-with-python-8-ways-to-do-linear-regression-and-measure-their-speed-b5577d75f8b>`__)
-
--  `Multi-variate regression with
-   regularization <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Multi-variate%20LASSO%20regression%20with%20CV.ipynb>`__
-
--  Polynomial regression using ***scikit-learn pipeline feature***
-   (`check the article I wrote on *Towards Data
-   Science* <https://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49>`__)
--  Decision trees and Random Forest regression (showing how the Random
-   Forest works as a robust/regularized meta-estimator rejecting
-   overfitting)
-
--  `Detailed visual analytics and goodness-of-fit diagnostic tests for a
-   linear regression
-   problem <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Regression_Diagnostics.ipynb>`__
-
---------------
-
-Classification
-===============
-
-.. image:: https://qph.fs.quoracdn.net/main-qimg-914b29e777e78b44b67246b66a4d6d71
-   :width: 500px
-   :align: center
-   :height: 350px
-   :alt: alternate text
-
--  Logistic regression/classification
-
--  *k*-nearest neighbor classification
--  Decision trees and Random Forest Classification
--  Support vector machine classification (`check the article I wrote
-   in Towards Data Science on SVM and sorting
-   algorithm <https://towardsdatascience.com/how-the-good-old-sorting-algorithm-helps-a-great-machine-learning-technique-9e744020254b>`__)
-
--  Naive Bayes classification
-
---------------
-
-Clustering
-===============
-
-.. image:: https://i.ytimg.com/vi/IJt62uaZR-M/maxresdefault.jpg
-   :width: 500px
-   :align: center
-   :height: 350px
-   :alt: alternate text
-
--  *K*-means clustering
--  Affinity propagation (showing its time complexity and the effect of
-   damping factor)
--  Mean-shift technique (showing its time complexity and the effect of
-   noise on cluster discovery)
--  DBSCAN (showing how it can generically detect areas of high density
-   irrespective of cluster shapes, which the k-means fails to do)
--  Hierarchical clustering with Dendograms showing how to choose optimal
-   number of clusters
-
---------------
-
-Dimensionality reduction
-===========================
-
-.. image:: https://i.ytimg.com/vi/QP43Iy-QQWY/maxresdefault.jpg
-   :width: 500px
-   :align: center
-   :height: 350px
-   :alt: alternate text
-
--  Principal component analysis
-
---------------
-
-Deep Learning/Neural Network
-==============================
-
--  `Demo notebook to illustrate the superiority of deep neural network
-   for complex nonlinear function approximation
-   task <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Function%20Approximation%20by%20Neural%20Network/Polynomial%20regression%20-%20linear%20and%20neural%20network.ipynb>`__
--  Step-by-step building of 1-hidden-layer and 2-hidden-layer dense
-   network using basic TensorFlow methods
-
---------------
-
-Random data generation using symbolic expressions
-======================================================
-
--  How to use `Sympy package <https://www.sympy.org/en/index.html>`__ to
-   generate random datasets using symbolic mathematical expressions.
-
--  Here is my article on Medium on this topic: `Random regression and
-   classification problem generation with symbolic
-   expression <https://towardsdatascience.com/random-regression-and-classification-problem-generation-with-symbolic-expression-a4e190e37b8d>`__
-
---------------
-
-Simple deployment examples (serving ML models on web API)
-============================================================
-
--  `Serving a linear regression model through a simple HTTP server
-   interface <https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/Linear_regression>`__.
-   User needs to request predictions by executing a Python script. Uses
-   ``Flask`` and ``Gunicorn``.
-
--  `Serving a recurrent neural network (RNN) through a HTTP
-   webpage <https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/rnn_app>`__,
-   complete with a web form, where users can input parameters and click
-   a button to generate text based on the pre-trained RNN model. Uses
-   ``Flask``, ``Jinja``, ``Keras``/``TensorFlow``, ``WTForms``.
-
---------------
-
-Object-oriented programming with machine learning
-======================================================
-
-Implementing some of the core OOP principles in a machine learning
-context by `building your own Scikit-learn-like estimator, and making it
-better <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/OOP_in_ML/Class_MyLinearRegression.ipynb>`__.
-
-See my articles on Medium on this topic.
-
--  `Object-oriented programming for data scientists: Build your ML
-   estimator <https://towardsdatascience.com/object-oriented-programming-for-data-scientists-build-your-ml-estimator-7da416751f64>`__
--  `How a simple mix of object-oriented programming can sharpen your
-   deep learning
-   prototype <https://towardsdatascience.com/how-a-simple-mix-of-object-oriented-programming-can-sharpen-your-deep-learning-prototype-19893bd969bd>`__
-
-.. |License| image:: https://img.shields.io/badge/License-BSD%202--Clause-orange.svg
-   :target: https://opensource.org/licenses/BSD-2-Clause
-.. |GitHub forks| image:: https://img.shields.io/github/forks/tirthajyoti/Machine-Learning-with-Python.svg
-   :target: https://github.com/tirthajyoti/Machine-Learning-with-Python/network
-.. |GitHub stars| image:: https://img.shields.io/github/stars/tirthajyoti/Machine-Learning-with-Python.svg
-   :target: https://github.com/tirthajyoti/Machine-Learning-with-Python/stargazers
+|License| |GitHub forks| |GitHub stars|
+
+Python Machine Learning Notebooks (Tutorial style)
+==================================================
+
+### Dr. Tirthajyoti Sarkar, Fremont, CA (`Please feel free to add me on LinkedIn here <https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7>`__)
+--------------------------------------------------------------------------------------------------------------------------------------------------
+
+Requirements
+~~~~~~~~~~~~
+
+-  **Python 3.5+**
+-  **NumPy (``$ pip install numpy``)**
+-  **Pandas (``$ pip install pandas``)**
+-  **Scikit-learn (``$ pip install scikit-learn``)**
+-  **SciPy (``$ pip install scipy``)**
+-  **Statsmodels (``$ pip install statsmodels``)**
+-  **MatplotLib (``$ pip install matplotlib``)**
+-  **Seaborn (``$ pip install seaborn``)**
+-  .. rubric:: **Sympy (``$ pip install sympy``)**
+      :name: sympy-pip-install-sympy
+
+You can start with this article that I wrote in Heartbeat magazine (on
+Medium platform): ### `“Some Essential Hacks and Tricks for Machine
+Learning with
+Python” <https://heartbeat.fritz.ai/some-essential-hacks-and-tricks-for-machine-learning-with-python-5478bc6593f2>`__
+
+Essential tutorial-type notebooks on Pandas and Numpy
+-----------------------------------------------------
+
+Jupyter notebooks covering a wide range of functions and operations on
+the topics of NumPy, Pandans, Seaborn, matplotlib etc.
+
+`Basic Numpy operations <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Basics%20of%20Numpy%20arrays.ipynb>`__
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`Basic Pandas operations <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Basics%20of%20Pandas%20DataFrame.ipynb>`__
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`Basics of visualization with Matplotlib and descriptive stats <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Basics%20of%20Matplotlib%20and%20Descriptive%20Statistics.ipynb>`__
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`Advanced Pandas operations <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Advanced%20Pandas%20Operations.ipynb>`__
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`How to read various data sources <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Read_data_various_sources/How%20to%20read%20various%20sources%20in%20a%20DataFrame.ipynb>`__
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`PDF reading and table processing demo <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Read_data_various_sources/PDF%20table%20reading%20and%20processing%20demo.ipynb>`__
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`How fast are Numpy operations compared to pure Python code? <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/How%20fast%20are%20NumPy%20ops.ipynb>`__ (Read my `article <https://towardsdatascience.com/why-you-should-forget-for-loop-for-data-science-code-and-embrace-vectorization-696632622d5f>`__ on Medium related to this topic)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+`Fast reading from Numpy using .npy file format <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_Reading.ipynb>`__ (Read my `article <https://towardsdatascience.com/why-you-should-start-using-npy-file-more-often-df2a13cc0161>`__ on Medium on this topic)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Tutorial-type notebooks covering regression, classification, clustering, dimensionality reduction, and some basic neural network algorithms
+-------------------------------------------------------------------------------------------------------------------------------------------
+
+Regression
+~~~~~~~~~~
+
+-  Simple linear regression with t-statistic generation
+
+-  `Multiple ways to perform linear regression in Python and their speed
+   comparison <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Linear_Regression_Methods.ipynb>`__
+   (`check the article I wrote on
+   freeCodeCamp <https://medium.freecodecamp.org/data-science-with-python-8-ways-to-do-linear-regression-and-measure-their-speed-b5577d75f8b>`__)
+
+-  `Multi-variate regression with
+   regularization <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Multi-variate%20LASSO%20regression%20with%20CV.ipynb>`__
+
+-  Polynomial regression using **scikit-learn pipeline feature** (`check
+   the article I wrote on Towards Data
+   Science <https://towardsdatascience.com/machine-learning-with-python-easy-and-robust-method-to-fit-nonlinear-data-19e8a1ddbd49>`__)
+
+-  Decision trees and Random Forest regression (showing how the Random
+   Forest works as a robust/regularized meta-estimator rejecting
+   overfitting)
+
+-  `Detailed visual analytics and goodness-of-fit diagnostic tests for a
+   linear regression
+   problem <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Regression_Diagnostics.ipynb>`__
+
+--------------
+
+Classification
+~~~~~~~~~~~~~~
+
+-  Logistic regression/classification
+
+-  *k*-nearest neighbor classification
+
+-  Decision trees and Random Forest Classification
+
+-  Support vector machine classification (`check the article I wrote in
+   Towards Data Science on SVM and sorting
+   algorithm <https://towardsdatascience.com/how-the-good-old-sorting-algorithm-helps-a-great-machine-learning-technique-9e744020254b>`__\ **)**
+
+-  Naive Bayes classification
+
+--------------
+
+Clustering
+~~~~~~~~~~
+
+-  *K*-means clustering
+-  Affinity propagation (showing its time complexity and the effect of
+   damping factor)
+-  Mean-shift technique (showing its time complexity and the effect of
+   noise on cluster discovery)
+-  DBSCAN (showing how it can generically detect areas of high density
+   irrespective of cluster shapes, which the k-means fails to do)
+-  Hierarchical clustering with Dendograms showing how to choose optimal
+   number of clusters
+
+--------------
+
+Dimensionality reduction
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+-  Principal component analysis
+
+--------------
+
+Deep Learning/Neural Network
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+-  `Demo notebook to illustrate the superiority of deep neural network
+   for complex nonlinear function approximation
+   task <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Function%20Approximation%20by%20Neural%20Network/Polynomial%20regression%20-%20linear%20and%20neural%20network.ipynb>`__
+-  Step-by-step building of 1-hidden-layer and 2-hidden-layer dense
+   network using basic TensorFlow methods
+
+--------------
+
+Random data generation using symbolic expressions
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+-  How to use `Sympy package <https://www.sympy.org/en/index.html>`__ to
+   generate random datasets using symbolic mathematical expressions.
+
+-  .. rubric:: Here is my article on Medium on this topic: `Random
+      regression and classification problem generation with symbolic
+      expression <https://towardsdatascience.com/random-regression-and-classification-problem-generation-with-symbolic-expression-a4e190e37b8d>`__
+      :name: here-is-my-article-on-medium-on-this-topic-random-regression-and-classification-problem-generation-with-symbolic-expression
+
+Simple deployment examples (serving ML models on web API)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+-  `Serving a linear regression model through a simple HTTP server
+   interface <https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/Linear_regression>`__.
+   User needs to request predictions by executing a Python script. Uses
+   ``Flask`` and ``Gunicorn``.
+
+-  `Serving a recurrent neural network (RNN) through a HTTP
+   webpage <https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Deployment/rnn_app>`__,
+   complete with a web form, where users can input parameters and click
+   a button to generate text based on the pre-trained RNN model. Uses
+   ``Flask``, ``Jinja``, ``Keras``/``TensorFlow``, ``WTForms``.
+
+--------------
+
+Object-oriented programming with machine learning
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Implementing some of the core OOP principles in a machine learning
+context by `building your own Scikit-learn-like estimator, and making it
+better <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/OOP_in_ML/Class_MyLinearRegression.ipynb>`__.
+
+See my articles on Medium on this topic.
+
+-  `Object-oriented programming for data scientists: Build your ML
+   estimator <https://towardsdatascience.com/object-oriented-programming-for-data-scientists-build-your-ml-estimator-7da416751f64>`__
+-  `How a simple mix of object-oriented programming can sharpen your
+   deep learning
+   prototype <https://towardsdatascience.com/how-a-simple-mix-of-object-oriented-programming-can-sharpen-your-deep-learning-prototype-19893bd969bd>`__
+
+.. |License| image:: https://img.shields.io/badge/License-BSD%202--Clause-orange.svg
+   :target: https://opensource.org/licenses/BSD-2-Clause
+.. |GitHub forks| image:: https://img.shields.io/github/forks/tirthajyoti/Machine-Learning-with-Python.svg
+   :target: https://github.com/tirthajyoti/Machine-Learning-with-Python/network
+.. |GitHub stars| image:: https://img.shields.io/github/stars/tirthajyoti/Machine-Learning-with-Python.svg
+   :target: https://github.com/tirthajyoti/Machine-Learning-with-Python/stargazers