1. You want to see smart computers? You need to check out machine learning!
Mickey Gousset
Principal Consultant / Team Lead
Infront Consulting Group
Mickeygousset.com
@mickey_gousset

2. What is Tupelo, MS “World-Famous” For?

3. Agenda What is Machine Learning DEMO Our First Machine Learning Model
Deploying The Model
Testing The Model
Using Python For Data Analysis

4. What is Machine Learning?
Computer "learns" from data in order to perform predictive analytics
Credit-card fraud detection
Online shopping recommendations
Self-driving cars and more
Supervised learning
Unsupervised learning
Machine Learning finds patterns in large volumes of data and uses those patterns to perform predictive analysis. Microsoft offers Azure Machine Learning, while Amazon offers Amazon Machine Learning and Google offers the Google Prediction API. Software products such as MATLAB support traditional, non-cloud-based ML modeling.
Machine learning models fall into two broad categories: supervised and unsupervised. In supervised learning, the model is "trained" with a large volume of data and algorithms are then used to predict an outcome from future inputs. Most supervised learning models use regression algorithms to compute an outcome from a continuous set of possible outcomes (for example, your score on a test), or classification algorithms to compute the probability of an outcome from a finite set of possible outcomes (for example, the probability that an is spam or a credit-card transaction is fraudulent). In unsupervised learning, the computer isn't trained, but is presented with a set of data and challenged to find relationships in it. K-Means Clustering is a common unsupervised learning algorithm. For a great explanation of how it works, see

5. Azure Machine Learning
Fully managed cloud service for building and operationalizing ML models
Fully managed
Integrated
Best in Class Algorithms + R
Deploy in minutes
No software to install, no hardware to manage, and one portal to view and update.
Azure Machine Learning is a cloud-based predictive-analytics service that offers a streamlined experience for data scientists of all skill levels. It's accompanied by the Azure Machine Learning Studio (ML Studio), which is a browser-based tool that provides an easy to use, drag-and-drop interface for building machine-learning models. It comes with a library of time-saving experiments and features best-in-class algorithms developed and tested in the real world by Microsoft businesses such as Bing. And its built-in support for R and Python means you can build custom scripts  to customize your model. Once you've built and trained your model in the ML Studio, you can easily expose it as a Web service that is consumable from a variety of programming languages, or share it with the community by placing it in the Cortana Intelligence Gallery.
Simple drag, drop, and connect interface for Data Science. No need for programming for common tasks.
Built-in collection of best of breed algorithms. Support for R and popular CRAN packages.
Operationalize models with a single click. Monetize in Machine Learning Marketplace.

6. Machine Learning Process

7. WOULD YOU HAVE SURVIVED THE TITANIC?

8. WOULD YOU BRIAN HAVE SURVIVED THE TITANIC?
I prebuilt some of this in ML_Testing
Would you have survived on the Titanic? This is a classic example that people use to teach Machine Learning, so I thought it would work well here. Let’s predict if my good friend Brian Randell would have survived on the Titanic.
Step 1: Get Access to Azure Machine Learning Studio
ML Studio is part of Microsoft Azure.
You can sign up as a guest for access, or get access using your Azure Subscription.
I’m going to use my azure subscription
New | Data + Analytics | Machine Learning Workspace Studio
Explain the different options
Launch the Machine Learning Workspace
Let’s Create Our Titanic Experiment
Click New | Blank Experiment
Change the title
The Data
Open the train data and explain the columns
Survivied – 1 = yes, 0 = no
Pclass – socioeconomic status: 1=upper, 2 = middle, 3 = lower
Sibsp – sibnling spouse
Parch – parent, child
New | DataSet | From Local File
Show how to do this, but don’t actually do it. We will use the preloaded file
Drag the data onto the experiment
Columns are called ‘Features”
Notice how you can drage around, zoom in and out
Right-click and visualize
One of the keys is to know your data
When you click a class, you can see stats to the right
We have some rows that are missing some data, and some data may not be needed
Manipulating the Data
Some columns are not meaningful in predicting what we care about.
Women/children were evacuated first, so age/gender is important.
PassengerID is just a primary key. It doesn’t matter. Neither does name.
Let’s pare down our dataset some
Use the search functionality to Drag a Select Columns In dataset task to the workspace
Connect the dataset to the new task – notice the red and green color coding
This task lets us specify the columns we think are significant, that we care about for the prediction. We also need to select the column we are going to predict “Survived”
Notice how the task as a red exclamation point meaning it needs to be addressed
Select the task and click Launch Column Selector
Can select columns by name or by rules
I’ll use by column
Survived
Pclass
Sex
Age
Parch
Sibsp
At this point I can run my model and look at the output of the select. Run and View
Best Practice do this every so often, as well as save
Visualize the data set. Notice that there is still data missing for Age. We want to pull out those rows.
Select Survived. Notice the graph. Because a passer either dies or doesn’t, there are no middle values. However, because this is a numeriv value, ML doesn’t know that. We need to make this (as well as Pclass, SibSP, Parch) categorical values. A categorical value is a value that can take on one of a limited (usually fixed) number of possible values.
Add Edit Metadata to Page
Connect Select Columns in Dataset to Edit Metadata
Select: Survivied, Pclass, Sibsp, Parch
We can use this task to change column names, and do other data modifications.
Select Make Categorical.
Save and test
Add a Clean Missing Data Task
Connect Edit Metadata to it
Select Age column and Remove Entire Row. Notice the other options
Save and Run
Split Data for Training vs Testing
Whenever we execute ML Experiments, we use some of the data to train the model and some to test it.
Split Data Task
Allows us to divie up the data, some to trya nd find patterns and some to test if the model we created was successful.
Do an 80/20 split and test
Creating the Model
So far we have done a bunch of data manipulation.
Now we can create the actual model
Add a Train Model task
Connect the Split Data to the Train model
Select the Survived column, to tell our model what we are training for
We need a model to train. At this point if you wanted you could import your own R code or Python code
But we are going to try some of the built in standard algorithms
Different types of ML use different algorithms. Since we are trying to predict if an output has one of two values, we want to use a two-class algorithm to train our model. Two class algorithms are used to predict outcomes that can only have two possible values.
Search two-class in the task bar. There are a number of different algorithms
ML Algorithm Cheat Sheet:
Open Cheat Sheet
Select Two Class Decision Forest and connect it to the training model. Take default values
After the model is trained, we need to see how well it predicts survival. So we need to score it against the 20% remainder of our data
Add a Score Model Task and link it up
Finall, we add an Evaluate Model task to evaluate the results
Run and Interpret The Results
Run the experiment
Visualize results of the run
First the Graph. The closer the graph is to a straight line, the more your model is guessing randomly. You want to get your line as close to the upper left corner as possible. You want as large an Area Under The Curve as possible. ROC – Reciever Operating Charactieric)
Scroll down for detailed results
The closer AUC is to 1, the better the model is at making predictions
Confusion Matrix
True Positive – how often the model correctly predicted someone would survive
False Positive – How often the model predticted someone would survive, when they did not (i.e. the model was incorrect)
True Negative – How often the model predicted a passenger would not survive
False Negative – how often the model predicted someone died when they actually survived. (i.e. the model was incorrect)
You want high values for True positive/negative, and low values for False Positive/Negative
Accuracy – Sum of all the correct predictions divided by the total number of predictions. Good value for evenly distributed data points but badly for skewed datapoints.
Precision – True Positive / TP + FP. This metric is concerned with the numner of correct positive predictions. “Of those predicted to the positive, how many were actually predicted correctly”
Recall – also called the True Positive Rate. TP / (TP+FN). Concerned with the number of correctly predicted positive events. “Of those positive events, how many were predticted correctly”
F1 Score – 2* (precision * recall) / precision * recall. Harmonic mean of precision and recall and is a good way to summaraize the evaluation of the algorithm in a single number
Precision vs recall
If precision or recall is high, indicates patients with breast cancer are diagnosed correctly
If precision is low, more patients without breast cancer are diagnosed as having cancer
If recall is low, more pateitns with brest cancer are diagnosed as not having cancer
At this point we have a working experiment and I’m happy with the accuracy. If I wasn’t, I could try other models or I could try more data. Now I want to use this to predict if Brian would survive the titanic.
I need to take my model and deploy it as a web service. Then I can have a website or app call it and see what happens
CREATING A WEB SERVICE
Convert the training experiement into a predictive experiment. This getst the model ready to be deployed as as web service.
Run my model one more time.
Click Setup Web service | Predictive Web Service
This creates a new Preditive experiment for your web service. The predictive model doesn’t have as many components as your original experiement
You don’t need a data set because when someone calls the web service they will pass in data
You still need to identify eh columns
Your algorithm and trainemodel tasks have become a single trained model
No need to evaluate. Just score at the end.
Web Service Input and Output
Run the experiement – this can be slow, so be prepared to talk
Click Deploy Web Service (new) Preview
Click Test Web Service
Batch test using the test.csv file
TESTING THE WEB SERVICE
Go to Consume. Open Excel, copy some data in and test
Select Python 3 code
Back in workspace, create a new notebook – Python 3
Copy in code and run
Need to look up more on jupyter notebooks
Let’s create a website where we can test this
Go to portla and create an Azure ML WeB App
WouldBrianSurvive.azurewebsites.net
Configure the site then demo it
Let’s go back and add another model to see if we can get a better result
As well as change it where it only asks for the values we need.
Add Two-Class Logitics Regression, Train Model, Score Model
We can see the second model has a lower accuracy. Let’s see if Brian survives using that model.
Change the web service input to Edit Metadata
Talk about the Cortana Intelligence Gallery:
Let’s Look At Another Example That Also Uses Some Python
Flight Delays and Jupyter Notebooks
Execute the runbook and look at the python output

9. Wrap-up Machine Learning is awesome, go try it!
Thank you for attending my talk!

10. Mickey Gousset Principal Consultant / Team Lead
Infront Consulting Group –
Microsoft MVP – Visual Studio Tools and Development Technologies – 13 Years
Author, Speaker, Father (2 beautiful daughters), Geek
Lives in Tupelo, Mississippi, USA