update

Author: astorfi

docs/build/doctrees/content/overview/linear-regression.doctree

@@ -32,7 +32,7 @@ shows a data set with a linear relationship.
 
    **Figure 1. A sample data set with a linear relationship** [`code`__]
 
-   .. __: /code/overview/linear_regression/linear_regression.py
+   .. __: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linear_regression.py
 
 Our goal is to find the line that best models the path of the data points 
 called a line of best fit. The equation in *Equation 1*, is an example of a
@@ -49,7 +49,7 @@ through it.
 
    **Figure 2. The data set from Figure 1 with a line of best fit** [`code`__]
 
-   .. __: /code/overview/linear_regression/linear_regression_lobf.py
+   .. __: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linear_regression_lobf.py
 
 Let’s break it down. We already know that x is the input value and y is our 
 predicted output. a₀ and a₁ describe the shape of our line. a₀ is called the 
@@ -80,7 +80,7 @@ relationship so linear regression would not be a good choice.
 
    **Figure3. A sample data set without a linear relationship** [`code`__]
 
-   .. __: /code/overview/linear_regression/not_linear_regression.py
+   .. __: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/not_linear_regression.py
 
 It is worth noting that sometimes you can apply transformations to data so 
 that it appears to be linear. For example, you could apply a logarithm to 
@@ -97,7 +97,7 @@ transformed to have a linear relationship.
 
    **Figure 4. A sample data set that follows an exponential curve** [`code`__]
 
-   .. __: /code/overview/linear_regression/exponential_regression.py
+   .. __: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/exponential_regression.py
 
 *Figure 5* is the same data after transforming the output variable with a 
 logarithm.
@@ -107,7 +107,7 @@ logarithm.
    **Figure 5. The data set from Figure 4 after applying a logarithm to the 
    output variable** [`code`__]
 
-   .. __: /code/overview/linear_regression/exponential_regression_transformed.py
+   .. __: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/exponential_regression_transformed.py
 
 
 *************
@@ -124,7 +124,7 @@ for one such prediction.
    **Figure 6. The plot from Figure 2 with the cost of one prediction 
    emphasized** [`code`__]
 
-   .. __: /code/overview/linear_regression/linear_regression_cost.py
+   .. __: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linear_regression_cost.py
 
 Two common terms that appear in cost functions are the **error** and 
 **squared error**. The error [*Equation 2*] is how far away from the actual 
@@ -206,12 +206,12 @@ Code
 ****
 This module's main code is available in the linear_regression_lobf.py_ file.
 
-.. _linear_regression_lobf.py: /code/overview/linear_regression/linear_regression_lobf.py
+.. _linear_regression_lobf.py: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linear_regression_lobf.py
 
 All figures in this module were created with simple modifications of the 
 linear_regression.py_ code.
 
-.. _linear_regression.py: /code/overview/linear_regression/linear_regression.py
+.. _linear_regression.py: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linear_regression.py
 
 In the code, we analyze a data set with a linear relationship. We split the 
 data into a training set to train our model and a testing set to test its 

docs/build/doctrees/content/supervised/bayes.doctree

@@ -121,11 +121,11 @@ when features are not counts and include decimal values.
    **Figure 2. A normal distribution with the iconic bell curve shape** 
    [`code`__]
 
-   .. __: /code/supervised/Naive_Bayes/bell_curve.py
+   .. __: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/Naive_Bayes/bell_curve.py
 
 The relevant code is available in the gaussian.py_ file.
 
-.. _gaussian.py: /code/supervised/Naive_Bayes/gaussian.py
+.. _gaussian.py: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/Naive_Bayes/gaussian.py
 
 In the code, we try and guess a color from given RGB percentages. We create
 some data to work with where each data point represents an RGB triple. The
@@ -154,7 +154,7 @@ should use a Bernoulli model instead.
 
 The relevant code is available in the multinomial.py_ file.
 
-.. _multinomial.py: /code/supervised/Naive_Bayes/multinomial.py
+.. _multinomial.py: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/Naive_Bayes/multinomial.py
 
 The code is based on our fruit example. In the code, we try and guess a fruit
 from given characteristics. We create some data to work with where each data
@@ -186,7 +186,7 @@ Bernoulli model.
 
 The relevant code is available in the bernoulli.py_ file.
 
-.. _bernoulli.py: /code/supervised/Naive_Bayes/bernoulli.py
+.. _bernoulli.py: https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/Naive_Bayes/bernoulli.py
 
 In the code, we try and guess if something is a duck or not based on certain
 characteristics it has. We create some data to work with where each data point

docs/build/doctrees/credentials/LICENSE.doctree

@@ -3,7 +3,7 @@ LICENSE
 
 MIT License
 
-Copyright (c) 2017 Amirsina Torfi
+Copyright (c) 2019 Amirsina Torfi
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

docs/build/doctrees/environment.pickle

@@ -217,7 +217,7 @@ <h2><a class="toc-backref" href="#id18">Overview</a><a class="headerlink" href="
 shows a data set with a linear relationship.</p>
 <div class="figure" id="id7">
 <img alt="../../_images/LR.png" src="../../_images/LR.png" />
-<p class="caption"><span class="caption-text"><strong>Figure 1. A sample data set with a linear relationship</strong> [<a class="reference external" href="/code/overview/linear_regression/linear_regression.py">code</a>]</span></p>
+<p class="caption"><span class="caption-text"><strong>Figure 1. A sample data set with a linear relationship</strong> [<a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linear_regression.py">code</a>]</span></p>
 <div class="legend">
 </div>
 </div>
@@ -232,7 +232,7 @@ <h2><a class="toc-backref" href="#id18">Overview</a><a class="headerlink" href="
 through it.</p>
 <div class="figure" id="id9">
 <img alt="../../_images/LR_LOBF.png" src="../../_images/LR_LOBF.png" />
-<p class="caption"><span class="caption-text"><strong>Figure 2. The data set from Figure 1 with a line of best fit</strong> [<a class="reference external" href="/code/overview/linear_regression/linear_regression_lobf.py">code</a>]</span></p>
+<p class="caption"><span class="caption-text"><strong>Figure 2. The data set from Figure 1 with a line of best fit</strong> [<a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linear_regression_lobf.py">code</a>]</span></p>
 <div class="legend">
 </div>
 </div>
@@ -258,7 +258,7 @@ <h2><a class="toc-backref" href="#id19">When to Use</a><a class="headerlink" hre
 relationship so linear regression would not be a good choice.</p>
 <div class="figure" id="id10">
 <img alt="../../_images/Not_Linear.png" src="../../_images/Not_Linear.png" />
-<p class="caption"><span class="caption-text"><strong>Figure3. A sample data set without a linear relationship</strong> [<a class="reference external" href="/code/overview/linear_regression/not_linear_regression.py">code</a>]</span></p>
+<p class="caption"><span class="caption-text"><strong>Figure3. A sample data set without a linear relationship</strong> [<a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/not_linear_regression.py">code</a>]</span></p>
 <div class="legend">
 </div>
 </div>
@@ -271,7 +271,7 @@ <h2><a class="toc-backref" href="#id19">When to Use</a><a class="headerlink" hre
 transformed to have a linear relationship.</p>
 <div class="figure" id="id11">
 <img alt="../../_images/Exponential.png" src="../../_images/Exponential.png" />
-<p class="caption"><span class="caption-text"><strong>Figure 4. A sample data set that follows an exponential curve</strong> [<a class="reference external" href="/code/overview/linear_regression/exponential_regression.py">code</a>]</span></p>
+<p class="caption"><span class="caption-text"><strong>Figure 4. A sample data set that follows an exponential curve</strong> [<a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/exponential_regression.py">code</a>]</span></p>
 <div class="legend">
 </div>
 </div>
@@ -280,7 +280,7 @@ <h2><a class="toc-backref" href="#id19">When to Use</a><a class="headerlink" hre
 <div class="figure" id="id12">
 <img alt="../../_images/Exponential_Transformed.png" src="../../_images/Exponential_Transformed.png" />
 <p class="caption"><span class="caption-text"><strong>Figure 5. The data set from Figure 4 after applying a logarithm to the
-output variable</strong> [<a class="reference external" href="/code/overview/linear_regression/exponential_regression_transformed.py">code</a>]</span></p>
+output variable</strong> [<a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/exponential_regression_transformed.py">code</a>]</span></p>
 <div class="legend">
 </div>
 </div>
@@ -295,7 +295,7 @@ <h2><a class="toc-backref" href="#id20">Cost Function</a><a class="headerlink" h
 <div class="figure" id="id13">
 <img alt="../../_images/Cost.png" src="../../_images/Cost.png" />
 <p class="caption"><span class="caption-text"><strong>Figure 6. The plot from Figure 2 with the cost of one prediction
-emphasized</strong> [<a class="reference external" href="/code/overview/linear_regression/linear_regression_cost.py">code</a>]</span></p>
+emphasized</strong> [<a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linear_regression_cost.py">code</a>]</span></p>
 <div class="legend">
 </div>
 </div>
@@ -365,9 +365,9 @@ <h3><a class="toc-backref" href="#id23">Gradient Descent</a><a class="headerlink
 </div>
 <div class="section" id="code">
 <h2><a class="toc-backref" href="#id24">Code</a><a class="headerlink" href="#code" title="Permalink to this headline">¶</a></h2>
-<p>This module’s main code is available in the <a class="reference external" href="/code/overview/linear_regression/linear_regression_lobf.py">linear_regression_lobf.py</a> file.</p>
+<p>This module’s main code is available in the <a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linear_regression_lobf.py">linear_regression_lobf.py</a> file.</p>
 <p>All figures in this module were created with simple modifications of the
-<a class="reference external" href="/code/overview/linear_regression/linear_regression.py">linear_regression.py</a> code.</p>
+<a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/overview/linear_regression/linear_regression.py">linear_regression.py</a> code.</p>
 <p>In the code, we analyze a data set with a linear relationship. We split the
 data into a training set to train our model and a testing set to test its
 accuracy. You may have guessed that the model used is based on linear

docs/build/html/_sources/content/overview/linear-regression.rst.txt

@@ -319,11 +319,11 @@ <h3><a class="toc-backref" href="#id12">Gaussian Model (Continuous)</a><a class=
 <div class="figure" id="id4">
 <img alt="../../_images/Bell_Curve.png" src="../../_images/Bell_Curve.png" />
 <p class="caption"><span class="caption-text"><strong>Figure 2. A normal distribution with the iconic bell curve shape</strong>
-[<a class="reference external" href="/code/supervised/Naive_Bayes/bell_curve.py">code</a>]</span></p>
+[<a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/Naive_Bayes/bell_curve.py">code</a>]</span></p>
 <div class="legend">
 </div>
 </div>
-<p>The relevant code is available in the <a class="reference external" href="/code/supervised/Naive_Bayes/gaussian.py">gaussian.py</a> file.</p>
+<p>The relevant code is available in the <a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/Naive_Bayes/gaussian.py">gaussian.py</a> file.</p>
 <p>In the code, we try and guess a color from given RGB percentages. We create
 some data to work with where each data point represents an RGB triple. The
 values of the triples are decimals ranging from 0 to 1 and each has a color
@@ -365,7 +365,7 @@ <h3><a class="toc-backref" href="#id13">Multinomial Model (Discrete)</a><a class
 </tr>
 </tbody>
 </table>
-<p>The relevant code is available in the <a class="reference external" href="/code/supervised/Naive_Bayes/multinomial.py">multinomial.py</a> file.</p>
+<p>The relevant code is available in the <a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/Naive_Bayes/multinomial.py">multinomial.py</a> file.</p>
 <p>The code is based on our fruit example. In the code, we try and guess a fruit
 from given characteristics. We create some data to work with where each data
 point is a triple representing characteristics of a fruit namely size, weight,
@@ -410,7 +410,7 @@ <h3><a class="toc-backref" href="#id14">Bernoulli Model (Discrete)</a><a class="
 </tr>
 </tbody>
 </table>
-<p>The relevant code is available in the <a class="reference external" href="/code/supervised/Naive_Bayes/bernoulli.py">bernoulli.py</a> file.</p>
+<p>The relevant code is available in the <a class="reference external" href="https://github.com/machinelearningmindset/machine-learning-course/blob/master/code/supervised/Naive_Bayes/bernoulli.py">bernoulli.py</a> file.</p>
 <p>In the code, we try and guess if something is a duck or not based on certain
 characteristics it has. We create some data to work with where each data point
 is a triple representing the characteristics: walks like a duck, talks like a

docs/build/html/_sources/content/supervised/bayes.rst.txt

@@ -170,7 +170,7 @@
   <div class="section" id="license">
 <h1>LICENSE<a class="headerlink" href="#license" title="Permalink to this headline">¶</a></h1>
 <p>MIT License</p>
-<p>Copyright (c) 2017 Amirsina Torfi</p>
+<p>Copyright (c) 2019 Amirsina Torfi</p>
 <p>Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the “Software”), to deal
 in the Software without restriction, including without limitation the rights

docs/build/html/_sources/credentials/LICENSE.rst.txt

@@ -3,7 +3,7 @@ LICENSE
 
 MIT License
 
-Copyright (c) 2017 Amirsina Torfi
+Copyright (c) 2019 Amirsina Torfi
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal