From e6747e09e31798ed18836843dd0b106da9226d18 Mon Sep 17 00:00:00 2001
From: tirthajyoti <tirthajyoti@gmail.com>
Date: Sat, 27 Jul 2019 11:20:02 -0700
Subject: [PATCH 1/5] Updated index.rst

---
 docs/index.rst | 396 +++++++++++++++++++++++--------------------------
 1 file changed, 185 insertions(+), 211 deletions(-)

diff --git a/docs/index.rst b/docs/index.rst
index 92d4e21..3266f5a 100644
--- a/docs/index.rst
+++ b/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

From 84cc9d3d4937ebfdf2dab5153cbb4cd24051ade2 Mon Sep 17 00:00:00 2001
From: tirthajyoti <tirthajyoti@gmail.com>
Date: Sat, 27 Jul 2019 11:22:45 -0700
Subject: [PATCH 2/5] deleted new index.rst

---
 docs/index.rst | 185 -------------------------------------------------
 1 file changed, 185 deletions(-)
 delete mode 100644 docs/index.rst

diff --git a/docs/index.rst b/docs/index.rst
deleted file mode 100644
index 3266f5a..0000000
--- a/docs/index.rst
+++ /dev/null
@@ -1,185 +0,0 @@
-|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

From c8ac31be28215fca21f704d3d7a8f01405de58b6 Mon Sep 17 00:00:00 2001
From: tirthajyoti <tirthajyoti@gmail.com>
Date: Sat, 27 Jul 2019 14:53:21 -0700
Subject: [PATCH 3/5] Added updated index.rst

---
 docs/index.rst | 214 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 214 insertions(+)
 create mode 100644 docs/index.rst

diff --git a/docs/index.rst b/docs/index.rst
new file mode 100644
index 0000000..41b5604
--- /dev/null
+++ b/docs/index.rst
@@ -0,0 +1,214 @@
+|License| |GitHub forks| |GitHub stars|
+
+Python Machine Learning Notebooks (Tutorial style)
+==================================================
+
+Authored and maintained by 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>`__
+
+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)
+
+Regression
+----------
+
+-  Simple linear regression with t-statistic generation (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Linear_Regression_Practice.ipynb>`__)
+
+-  Multiple ways to perform linear regression in Python and their speed
+   comparison (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Linear_Regression_Methods.ipynb>`__).
+   Also `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 (`Here is the
+   Notebook <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** (`Here
+   is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Regularized%20polynomial%20regression%20with%20linear%20and%20random%20sampling.ipynb>`__).
+   Also `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) (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Random_Forest_Regression.ipynb>`__).
+
+-  Detailed visual analytics and goodness-of-fit diagnostic tests for a
+   linear regression problem (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Regression_Diagnostics.ipynb>`__).
+
+--------------
+
+Classification
+--------------
+
+-  Logistic regression/classification (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Logistic_Regression_Classification.ipynb>`__).
+
+-  *k*-nearest neighbor classification (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/KNN_Classification.ipynb>`__).
+
+-  Decision trees and Random Forest Classification (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/DecisionTrees_RandomForest_Classification.ipynb>`__).
+
+-  Support vector machine classification (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Support_Vector_Machine_Classification.ipynb>`__).
+   Also `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 (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Naive_Bayes_Classification.ipynb>`__).
+
+--------------
+
+Clustering
+----------
+
+-  *K*-means clustering (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/K_Means_Clustering_Practice.ipynb>`__).
+
+-  Affinity propagation (showing its time complexity and the effect of
+   damping factor) (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Affinity_Propagation.ipynb>`__).
+
+-  Mean-shift technique (showing its time complexity and the effect of
+   noise on cluster discovery) (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Mean_Shift_Clustering.ipynb>`__).
+
+-  DBSCAN (showing how it can generically detect areas of high density
+   irrespective of cluster shapes, which the k-means fails to do) (`Here
+   is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/DBScan_Clustering.ipynb>`__).
+
+-  Hierarchical clustering with Dendograms showing how to choose optimal
+   number of clusters (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Hierarchical_Clustering.ipynb>`__).
+
+--------------
+
+Dimensionality reduction
+------------------------
+
+-  Principal component analysis (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Principal%20Component%20Analysis.ipynb>`__)
+
+--------------
+
+Random data generation using symbolic expressions
+-------------------------------------------------
+
+-  Simple script to generate random polynomial expression/function
+   (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Random%20Function%20Generator/Random_function_generator.ipynb>`__).
+
+-  How to use `Sympy package <https://www.sympy.org/en/index.html>`__ to
+   generate random datasets using symbolic mathematical expressions
+   (`Here is the
+   Notebook <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Random%20Function%20Generator/Symbolic%20regression%20classification%20generator.ipynb>`__).
+   Also, `here is the Python
+   script <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Random%20Function%20Generator/Symbolic_regression_classification_generator.py>`__
+   if anybody wants to use it directly in their project.
+
+-  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>`__.
+
+`Here is the complete Python script with the linear regression
+class <https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/OOP_in_ML/Class_MyLinearRegression.py>`__,
+which can do fitting, prediction, cpmputation of regression metrics,
+plot outliers, plot diagnostics (linearity, constant variance, etc.),
+compute variance inflation factors.
+
+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

From 8ba34c4181e1fdaaa61a0507770c6927dcafc19f Mon Sep 17 00:00:00 2001
From: Tirthajyoti Sarkar <tirthajyoti@gmail.com>
Date: Sat, 27 Jul 2019 14:59:28 -0700
Subject: [PATCH 4/5] Update index.rst

---
 docs/index.rst | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/docs/index.rst b/docs/index.rst
index 41b5604..0595ee9 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -9,6 +9,10 @@ here <https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7>`__.
 
 --------------
 
+.. image:: https://miro.medium.com/max/1838/1*92h6Lg1Bu1F9QqoVNrkLdQ.jpeg
+   :width: 500px
+   :height: 450px
+
 Requirements
 ------------
 

From 7b6250e3db39aaa3a3068f6b24ca3cc0a0a273b1 Mon Sep 17 00:00:00 2001
From: Tirthajyoti Sarkar <tirthajyoti@gmail.com>
Date: Sun, 21 Mar 2021 15:42:13 -0700
Subject: [PATCH 5/5] Update README.md

---
 README.md | 98 ++++++++++++++++++++++++++++++++++++-------------------
 1 file changed, 65 insertions(+), 33 deletions(-)

diff --git a/README.md b/README.md
index 3f96239..e9145c9 100644
--- a/README.md
+++ b/README.md
@@ -1,22 +1,36 @@
 [![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)
 [![GitHub forks](https://img.shields.io/github/forks/tirthajyoti/Machine-Learning-with-Python.svg)](https://github.com/tirthajyoti/Machine-Learning-with-Python/network)
 [![GitHub stars](https://img.shields.io/github/stars/tirthajyoti/Machine-Learning-with-Python.svg)](https://github.com/tirthajyoti/Machine-Learning-with-Python/stargazers)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/tirthajyoti/Machine-Learning-with-Python/pulls)
 
-# Python Machine Learning Notebooks (Tutorial style)
+# Python Machine Learning Jupyter Notebooks ([ML website](https://machine-learning-with-python.readthedocs.io/en/latest/))
+
+### Dr. Tirthajyoti Sarkar, Fremont, California ([Please feel free to connect on LinkedIn here](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7))
 
-### 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`)**
+## Also check out these super-useful Repos that I curated
+
+[Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning](https://github.com/tirthajyoti/Papers-Literature-ML-DL-RL-AI)
+
+[Carefully curated resource links for data science in one place](https://github.com/tirthajyoti/Data-science-best-resources)
+
+## Requirements
+* **Python 3.6+**
+* **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`)**
+* **Flask (`pip install flask`)**
+* **WTForms (`pip install wtforms`)**
+* **Tensorflow (`pip install tensorflow>=1.15`)**
+* **Keras (`pip install keras`)**
+* **pdpipe (`pip install pdpipe`)**
+
 ---
 
 You can start with this article that I wrote in Heartbeat magazine (on Medium platform): 
@@ -24,16 +38,17 @@ You can start with this article that I wrote in Heartbeat magazine (on Medium pl
 <img src="https://cookieegroup.com/wp-content/uploads/2018/10/2-1.png" width="450" height="300"/>
 
 ## 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)
+Jupyter notebooks covering a wide range of functions and operations on the topics of NumPy, Pandans, Seaborn, Matplotlib etc.
+
+* [Detailed Numpy operations](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_operations.ipynb)
+* [Detailed Pandas operations](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Pandas_Operations.ipynb)
+* [Numpy and Pandas quick basics](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Numpy_Pandas_Quick.ipynb)
+* [Matplotlib and Seaborn quick basics](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Pandas%20and%20Numpy/Matplotlib_Seaborn_basics.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
 
@@ -47,34 +62,44 @@ Jupyter notebooks covering a wide range of functions and operations on the topic
 <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/f/f8/L1_and_L2_balls.svg/300px-L1_and_L2_balls.svg.png"/>
 
 * 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)
+
+* [Decision trees and Random Forest regression](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Random_Forest_Regression.ipynb) (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)
 
+* [Robust linear regression using `HuberRegressor` from Scikit-learn](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Regression/Robust%20Linear%20Regression.ipynb)
+
 -----
 
 ### Classification
-* Logistic regression/classification
+* Logistic regression/classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Logistic_Regression_Classification.ipynb))
 <img src="https://qph.fs.quoracdn.net/main-qimg-914b29e777e78b44b67246b66a4d6d71"/>
 
-* _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))**
+* _k_-nearest neighbor classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/KNN_Classification.ipynb))
+
+* Decision trees and Random Forest Classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/DecisionTrees_RandomForest_Classification.ipynb))
+
+* Support vector machine classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Support_Vector_Machine_Classification.ipynb)) (**[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))**
 
 <img src="https://docs.opencv.org/2.4/_images/optimal-hyperplane.png"/>
 
-* Naive Bayes classification
+* Naive Bayes classification ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Classification/Naive_Bayes_Classification.ipynb))
 
 ---
 
 ### Clustering
 <img src="https://i.ytimg.com/vi/IJt62uaZR-M/maxresdefault.jpg" width="450" height="300"/>
 
-* _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
+* _K_-means clustering ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/K_Means_Clustering_Practice.ipynb))
+
+* Affinity propagation (showing its time complexity and the effect of damping factor) ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Affinity_Propagation.ipynb))
+
+* Mean-shift technique (showing its time complexity and the effect of noise on cluster discovery) ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Mean_Shift_Clustering.ipynb))
+
+* DBSCAN (showing how it can generically detect areas of high density irrespective of cluster shapes, which the k-means fails to do) ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/DBScan_Clustering.ipynb))
+
+* Hierarchical clustering with Dendograms showing how to choose optimal number of clusters ([Here is the Notebook](https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Clustering-Dimensionality-Reduction/Hierarchical_Clustering.ipynb))
+
 <img src="https://www.researchgate.net/profile/Carsten_Walther/publication/273456906/figure/fig3/AS:294866065084419@1447312956501/Example-of-hierarchical-clustering-clusters-are-consecutively-merged-with-the-most.png" width="700" height="400"/>
 
 ---
@@ -96,8 +121,12 @@ Jupyter notebooks covering a wide range of functions and operations on the topic
 * 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)
+
 ---
 
+### Synthetic data generation techniques
+* [Notebooks here](https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Synthetic_data_generation)
+
 ### 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`.
 
@@ -113,3 +142,6 @@ 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)
 
+---
+### Unit testing ML code with Pytest
+Check the files and detailed instructions in the [Pytest](https://github.com/tirthajyoti/Machine-Learning-with-Python/tree/master/Pytest) directory to understand how one should write unit testing code/module for machine learning models