Machine Learning with R - Home - Matthewrenze · About Me Data Science Consultant Education B.S. in...
Transcript of Machine Learning with R - Home - Matthewrenze · About Me Data Science Consultant Education B.S. in...
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Machine Learning with R
@MatthewRenze
@netcorebcn
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John
Jane
Miko
Lee
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Job Postings for Machine Learning
Source: Indeed.com
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Source: Stack Overflow 2017
Average Salary by Job Type (USA)
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Overview
1. Introduction to ML
2. Introduction to R
3. Classification
4. Regression
5. Beyond the Basics
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About Me
Data Science Consultant
Education
B.S. in Computer Science
B.A. in Philosophy
Community
Public Speaker
Pluralsight Author
Microsoft MVP
ASPInsider
Open-source Software
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How Does This Apply to Me?
Make decisions using data
Make predictions using data
Make recommendations using data
Find patterns of interest in data
Find anomalies in data
Write code that does all these things
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Introduction to Machine Learning
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What is Machine Learning?
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Artificial
IntelligenceStatistics
Machine
Learning
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𝑓 𝑥
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DataPredictionFunction
𝑓 𝑥
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DataPredictionFunction
𝑓 𝑥
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DataPredictionFunction
Cat Dog
𝑓 𝑥
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DataPredictionFunction
Cat Dog
𝑓 𝑥
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DataPredictionFunction
Cat Dog Is cat?
𝑓 𝑥
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DataPredictionFunction
Cat Dog Is cat?
𝑓 𝑥
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DataPredictionFunction
Cat Dog Is cat? Yes
𝑓 𝑥
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Find a question
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Find a question
Prepare the data
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Find a question
Prepare the data
Train the model
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Find a question
Prepare the data
Train the model
Evaluate the
model
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Find a question
Prepare the data
Train the model
Evaluate the
model
Deploy the
model
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Find a question
Prepare the data
Train the model
Evaluate the
model
Deploy the
model
Monitor the
model
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Find a question
Prepare the data
Train the model
Evaluate the
model
Deploy the
model
Monitor the
model
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Data
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Data
Training
Test
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ML
Algorithm
Data
Training
Test
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ML
Algorithm
ML
Model
Data
Training
Test
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ML
Algorithm
ML
Model
Data
Training
Test
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ML
Algorithm
ML
Model
Data
Training
Test
New Data
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ML
Algorithm
ML
Model
Data
Training
Test
New Data
Prediction
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What Can Machine Learning Do?
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𝑓 𝑥1.23
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Source: Futurama
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Introduction to R
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What is R?
Open source
Language and environment
Numerical and graphical analysis
Cross platform
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What is R?
Active development
Large user community
Modular and extensible
9000+ extensions
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FREE
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FREE
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Code Demo
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Classification
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𝑓 𝑥
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Correct Spelling Ratio
Co
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t o
f S
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m W
ord
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Correct Spelling Ratio
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Correct Spelling Ratio
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Correct Spelling Ratio
Co
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Correct Spelling Ratio
Co
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Correct Spelling Ratio
Co
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Classification Algorithms
k-Nearest Neighbor Classifier
Decision Tree Classifier
Naïve Bayes Classifier
Support Vector Machine
Neural Network Classifier
x1
x2
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Decision Tree Classifier
Supervised learning
is sex male?
is age > 9.5?
is family > 2.5?
SurvivedDied
Died
Survived
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Decision Tree Classifier
Supervised learning
Tree of decisions
is sex male?
is age > 9.5?
is family > 2.5?
SurvivedDied
Died
Survived
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Decision Tree Classifier
Supervised learning
Tree of decisions
Information gain
is sex male?
is age > 9.5?
is family > 2.5?
SurvivedDied
Died
Survived
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Decision Tree Classifier
Supervised learning
Tree of decisions
Information gain
Simple and easy
is sex male?
is age > 9.5?
is family > 2.5?
SurvivedDied
Died
Survived
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Titanic Passenger Manifest
Name Gender Age Family Survived
Elizabeth Allen Female 29 0 Yes
Hudson Allison Jr. Male 1 3 Yes
Helen Allison Female 2 3 No
Hudson Allison Sr. Male 30 3 No
Bessie Allison Female 25 3 No
… … … …
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is sex male?
is age > 9.5?
is family > 2.5?
SurvivedDied
Died
Survived
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Neural Network Classifier
Supervised learning
Source: Wikipedia
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Neural Network Classifier
Supervised learning
Neurons in a brain
Source: Wikipedia
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Neural Network Classifier
Supervised learning
Neurons in a brain
Complex
Source: Wikipedia
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Neural Network Classifier
Supervised learning
Neurons in a brain
Complex
Not transparent
Source: Wikipedia
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Real-World Examples
Should we approve this loan?
Will this customer buy from us?
Should we replace this part?
Does this person have cancer?
x1
x2
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Iris Data Set
Iris Setosa Iris Versicolor Iris Virginica
Photos by Radomił Binek, Danielle Langlois, and Frank Mayfield
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Fisher’s Iris Data
Species Petal Length Petal Width Sepal Length Sepal Width
setosa 1.1 0.1 4.3 3
setosa 1.4 0.2 4.4 2.9
setosa 1.3 0.2 4.4 3
setosa 1.3 0.2 4.4 3.2
setosa 1.3 0.3 4.5 2.3
… … … …
Iris Data Set
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Classification Demo
Goal: Predict species based on petal and sepal measurements
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Regression
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𝑓 𝑥 1.23
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Area
Sa
le P
rice
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Sa
le P
rice
Area
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Area
Sa
le P
rice
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Regression Algorithms
Linear Regression
Polynomial Regression
Lasso Regression
ElasticNet Regression
Neural Network Regression
x1
x2
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Simple Linear Regression
Relationship
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Simple Linear Regression
Relationship
Linear model
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Simple Linear Regression
Relationship
Linear model
Explanatory variable
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Simple Linear Regression
Relationship
Linear model
Explanatory variable
Outcome variable
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Simple Linear Regression
Linear predictor function
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Simple Linear Regression
Linear predictor function
y = m · x + b
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Simple Linear Regression
Linear predictor function
y = m · x + b
Parameters estimated
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Simple Linear Regression
Linear predictor function
y = m · x + b
Parameters estimated
Relies on assumptions
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Neural Network Regression
Same as before
Numeric vs. Categorical
Source: Wikipedia
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Real-World Examples
How much profit will we make?
What will the price be tomorrow?
How many will this person buy?
How long until this part fails?
x1
x2
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Regression Demo
Goal: Predict petal width of Iris flowers
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Beyond the Basics
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This is just the tip of the iceberg!This is just the tip of the iceberg!
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Find a question
Prepare the data
Train the model
Evaluate the
model
Deploy the
model
Monitor the
model
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Creating accurate and robust models is not easy
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Find a question
Prepare the data
Train the model
Evaluate the
model
Deploy the
model
Monitor the
model
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Cleaning and Transforming Data
Data are messy
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Cleaning and Transforming Data
Data are messy
80% of work
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Cleaning and Transforming Data
Data are messy
80% of work
R helps a lot
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Cleaning and Transforming Data
Data are messy
80% of work
R helps a lot
Record all steps
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Goodness of Fit
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Underfit
Goodness of Fit
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Underfit Overfit
Goodness of Fit
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Underfit Good fit Overfit
Goodness of Fit
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Deep Learning
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John
Jane
Miko
Lee
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𝑓 𝑥1.23
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𝑓 𝑥1.23
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𝑓 𝑥1.23
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Source: YOLO: Real-Time Object Detection
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Source: http://grail.cs.washington.edu/projects/AudioToObama/ Source: Nvidia
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Source: http://grail.cs.washington.edu/projects/AudioToObama/
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Source: Pouff - Grocery TripSource: Google Deep Mind
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Source: Boston Dynamics
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Practical Demo
Goal: Predict who will survive the Titanic
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Conclusion
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Where to Go Next
Pluralsight: https://www.pluralsight.com
Data Camp: https://www.datacamp.com
Coursera: https://www.coursera.org
Tensorflow: http://playground.tensorflow.org
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www.pluralsight.com/authors/matthew-renze
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www.matthewrenze.com
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Feedback
Very important to me!
What did you like?
What could I improve?
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Conclusion
1. Introduction to ML
2. Introduction to R
3. Classification
4. Regression
5. Beyond the Basics
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Are you prepared?
Is your organization?
Is our world prepared?
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Contact Info
Matthew Renze
Data Science Consultant
Renze Consulting
Twitter: @matthewrenze
Email: [email protected]
Website: www.matthewrenze.com
Thank You! : )