Added tutorial conversion ML-Python

Author: kah3f

content/courses/python-high-performance/_index.md

@@ -28,7 +28,7 @@ For this tutorial, it is assumed that you have experience with programming in Py
 
 To follow along for the [Serial Optimization](#serial-optimization-strategies) and [Multiprocessing](#multiprocessing) examples, you can execute the code examples on your own computer or on UVA's high-performance computing cluster.  Examples described in the last section, [Distributed Parallelization](#distributed-parallelization), are best executed on UVA's high-performance computing platform.
 
-If you are using your local computer for your personal applications, not related to work, you can install the Anaconda distribution (<a href="https://www.anaconda.com/distribution/" target="balnk_">download</a>) to run the code examples. Anaconda provides multiple Python versions, an integrated development environment (IDE) with editor and profiler, Jupyter notebooks, and an easy-to-use package environment manager.  If you will or might use the installation for work, or just prefer a more minimal setup that you can more easily customize, we suggest Miniforge (https://github.com/conda-forge/miniforge).
+If you are using your local computer for your personal applications, not related to work, you can install the [Anaconda](https://www.anaconda.com) distribution to run the code examples. Anaconda provides multiple Python versions, an integrated development environment (IDE) with editor and profiler, Jupyter notebooks, and an easy-to-use package environment manager.  If you will or might use the installation for work, or just prefer a more minimal setup that you can more easily customize, we suggest Miniforge (https://github.com/conda-forge/miniforge).
 
 **If you are using UVA HPC, follow these steps to verify that your account is active:**
 

content/notes/python-machine-learning/_index.md

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+---
+title: Machine Learning for Python
+date: "2022-06-09T00:00:00"
+type: docs 
+weight: 1 
+
+
+menu: 
+    python-machine-learning:
+---
+
+In this tutorial we will be covering the following topics:
+* Overview of Machine Learning
+* Decision Trees
+    * Coding Decision Trees
+* Random Forest
+    * Coding Random Forest
+* Overview of Neural Networks
+    * Coding Neural Networks
+* Tensorflow/Keras
+    * Coding Tensorflow
+* PyTorch
+    * Coding PyTorch
+* Overview of Parallelizing Deep Learning
+    * Coding 
+
+As mentioned above, example codes will be provided for respective topics. Prior experience with the Python programming language and some familiarity with machine learning concepts are helpful for this tutorial. Please download and unzip the following file to follow along on code activities. 
+
+{{< file-download file="notes/python-machine-learning/code/ML_with_Python.zip" text="ML_with_Python.zip" >}}

content/notes/python-machine-learning/code/ML_with_Python.zip

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+---
+title: Decision Trees
+date: "2022-06-09T00:00:00"
+type: docs 
+toc: true
+weight: 150
+menu: 
+    python-machine-learning:
+---
+
+
+Decision trees are a classification algorithm within supervised learning. The algorithm determines a set of questions or tests that will guide it toward a classification of an observation and it organizes a series of attribute tests into a tree-structure to help determine classification of the unlabeled data.
+
+> Motivating Question:
+> Given a set of data, can we determine which attributes should be tested first to predict a category or outcome (i.e., which attributes lead to "high information gain")?
+
+## Simple Scenario 
+
+Suppose we have:
+* a group of people, each one with a tumor, and
+* two measurements (x, y) for each tumor.
+
+Plotting the data, and coloring the points red for malignant tumors and blue for benign tumors, we might see a plot as follows:
+
+{{< figure src=/notes/python-machine-learning/img/pre_decision_plot.png caption="" width=60% height=60% >}}
+
+Clearly, something happens near x=3.
+
+{{< figure src=/notes/python-machine-learning/img/decision_plot.png caption="" width=60% height=60% >}}
+
+With very few errors, we can use x=3 as our "decision" to categorize the tumor as malignant versus benign.
+
+__Resulting decision tree:__
+
+{{< figure src=/notes/python-machine-learning/img/result_decision_tree.png caption="" width=30% height=30% >}}
+
+Unfortunately, it is not always this easy, especially if we have much more complex data. More layers of questions can be added with more attributes.
+
+
+## Example: What should you do this weekend?
+
+{{< table >}} 
+| Weather | Parents Visiting | Have extra cash | Weekend Activity |
+| :-: | :-: | :-: | :-: |
+| Sunny | Yes | Yes | Cinema |
+| Sunny | No | Yes | Tennis |
+| Windy | Yes | Yes | Cinema |
+| Rainy | Yes | No | Cinema |
+| Rainy | No | Yes | Stay In |
+| Rainy | Yes | No | Cinema |
+| Windy | No | No | Cinema |
+| Windy | No | Yes | Shopping |
+| Windy | Yes | Yes | Cinema |
+| Sunny | No | Yes | Tennis |
+{{< /table >}}
+
+This table can be represented as a tree. 
+
+{{< figure src=/notes/python-machine-learning/img/tree_first.png caption="" width=65% height=65% >}}
+
+This tree can be made more efficient. 
+
+{{< figure src=/notes/python-machine-learning/img/tree_second.png caption="" width=50% height=50% >}}
+
+Also with complex data, it is possible that not all features are needed in the Decision Tree.
+
+## Decision Tree Algorithms
+
+There are many existing Decision Tree algorithms. If written correctly, the algorithm will determine the best question/test for the tree.
+
+> How do we know how accurate our decision tree is?
+
+## Decision Tree Evaluation
+
+* A confusion matrix is often used to show how well the model matched the actual classifications.
+  * The matrix is not confusing – it simply illustrates how "confused" the model is!
+* It is generated based on test data.
+
+{{< figure src=/notes/python-machine-learning/img/decision_tree_chart.png caption="" width=70% height=70% >}}
+
+

content/notes/python-machine-learning/decision_trees.md

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+---
+title: Coding a Decision Tree
+date: "2022-06-09T00:00:00"
+type: docs 
+weight: 200
+menu: 
+    python-machine-learning:
+      parent: Decision Trees
+---
+
+
+## The Data
+
+* For our first example, we will be using a set of measurements taken on various red wines.
+* The data set is from
+  * _P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009._
+* The data is located at
+  * [https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv](https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv)
+* There are 12 measurements, taken on 1599 different red wines.
+
+## Attribute Summary
+
+{{< figure src=/notes/python-machine-learning/img/attribute_summary.png caption="" width=50% height=50% >}}
+
+__Question: Can we predict the quality of the wine from the attributes?__
+
+## Coding Decision Trees:  General Steps
+1. Load the decision tree packages
+2. Read in the data
+3. Identify the target feature
+4. Divide the data into a training set and a test set.
+5. Fit the decision tree model
+6. Apply the model to the test data
+7. Display the confusion matrix
+
+
+### 1. Load Decision Tree Package
+```python
+from sklearn import tree
+```
+
+### 2. Read in the data
+```python
+import pandas as pd
+data_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
+wine = pd.read_csv(data_url, delimiter=';')
+print(wine.info())
+```
+
+### 3. Identify the target feature
+```python
+#Split the quality column out of the data
+wine_target = wine['quality']
+wine_data = wine.drop('quality', axis=1)
+```
+For the functions that we will be using, the target values (e.g., quality) must be a separate object.
+
+### 4. Divide the Data
+```python
+from sklearn import model_selection
+test_size = 0.30
+seed = 7
+train_data, test_data, train_target, test_target = model_selection.train_test_split(wine_data,
+wine_target, test_size=test_size,
+random_state=seed)
+```
+
+### 5. Fit the Decision Tree Model
+```python
+model = tree.DecisionTreeClassifier()
+model = model.fit(train_data, train_target)
+```
+
+### 6. Apply the Model to the Test Data
+```python
+prediction = model.predict(test_data)
+```
+
+### 7. Display Confusion Matrix
+```python
+row_name ="Quality"
+cm = pd.crosstab(test_target, prediction,
+rownames=[row_name], colnames=[''])
+print(' '*(len(row_name)+3),"Predicted ", row_name)
+print(cm)
+```
+
+## Activity:  Decision Tree Program
+
+Make sure that you can run the decisionTree code: `01_Decision_Tree.ipynb`
+