This slide deck is from a webinar "Everything you always wanted to know about Machine Learning but did not know where to ask"

1. Webinar: Machine Learning with Spark
Everything you want to know about Machine Learning
but could not find the place and time to ask

2. Highlights
 Detecting the low hanging fruit for machine learning
 Balancing business and science on your team
 Choosing the best Machine Learning tools, be it small or Big
Data, R, Python or Spark. (And these are not mutually
exclusive).
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3. What does a data scientist need to know
 Familiarity with either Java / Scala / Python language
– Need to be comfortable programming - there are many labs
– Our platform is Spark, basic familiarity is expected
– Our labs are in Scala, basics of Scala will be helpful
 Basic understanding of Linux development environment
– Command line navigation
– Editing files (e.g. using VI or nano)
 This is a Machine Learning with Spark class
– But, no previous of Machine Learning knowledge is assumed
– Class will be paced based on the pace of majority of the
students.
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4. Lots of Labs : Learn By Doing
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Where is
the ANY
key?

5. After The Class…
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Machine
Learning

6. Recommended Books
 “Advanced Analytics With Spark” by Sandy Ryza, et al.
 ”Data Algorithms” by Mahmoud Parsian
 “Computational Complexity - A Modern Approach” by Sanjeev
Arora and Boaz Barak
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7. Why machine learning?
 Build a model to detect credit card fraud
– thousands of features
– billions of transactions
 Recommend
– millions of products
– to millions of users
 Estimate financial risk
– simulations of portfolios
– With millions of instruments
 Genome data manipulation
– thousands of human genomes
– detect genetic associations with disease
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8.  Like Hadoop MapReduce, Spark has linear scalability and
fault tolerance for large data sets
 However, it adds the following extensions
– DAG of operations, instead of Map-then-Reduce
– Rich transformations to express solutions in the natural way
– RDD – in-memory computation
 Addresses the major bottleneck:
– Not CPU
– Not disk
– Not network
– But developer productivity
Why Spark?

9. The story of Spark

10.  It reduces performance overhead
– Be certain the performance adequate
– Scala gives you access to the latest and greatest
– Python and R bindings may come much later
 Scala helps you understand the Spark approach better
– Spark is written in Scala
– Think in Scala, think in Spark
 Just Scala, no other languages needed
– Such as R with SQL
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Why Scala?

11.  Python
– Popular, well-known
– Many packages
– Graphing
 R
– Very popular, well-known
– Very many packages
– Graphing
Why NOT Scala?

12. About Machine Learning
 What is Machine Learning?
 It is an algorithm that “learns” from data
– Any algorithm which improves its performance by access to data.
 Machine Learning borrows from applied statistics
 Also considered a branch of AI (Artificial Intelligence)
12

13.  Sixties
– Commercial computers & mainframes
– Computers play chess
 Eighties
– Computational complexity theory
– Artificial intelligence (AI) gets a bad rap
 21st century
– Big Data changes it all
A glimpse of history

14.  Computational complexity is simple:
 P – all problems that can be solved fast
– (in polynomial time, like n^p, but not exponential)
– Example: system of linear equations
 NP – all problems that can be verified fast
– That is, just check if the solution is correct
 But folks, it does not matter!
P = NP?

15.  “Big O” notation
 Example of polynomial time O(n^^3)
 Example of exponential time O(2^^n)
– How much is that?
– Compare to the number of particles in the universe ~ 10^^80
– To reach that, our n needs to be log(10^^80)
= 80 log (10) ~ 80 * 3 = 240
 There are also in-between, such as n^^(log log (n))
– But that is still bad enough
O(n) notation

16.  Old reasons
– It is too theoretical, talking only about worst case scenario
– There may be new computers, such as quantum computers
 New reason
– Big Data
– Turing machine is inadequate
• Because we hit the size limitations of one computer
• And go into clusters
• And we have other problems than expected
Copyrig
ht ©
2016
Elephant
Scale.
All
rights
reserved
.
Why P and NP do not matter

17.  Old thinking:
– If you can solve any problem (P = NP), you can be creative
 New thinking:
– You don’t have to solve problems in order to be creative
– Instead, you can pick up the answer from the internet 
– Examples:
• Google translate
• IBM Dr. Watson (Jeopardy winner)
• Lesson: re-use world’s data
 New thinking:
– Rely on the abundance of data
– Find an approximate solution that is good enough
– “Bad algorithms trained on lots of data can outperform good ones
trained on very little” - Deeplearningfor4
How Big Data changed it all

18.  Turing machine might be too theoretical
 But developers often tend to “just code”
“Думать не за свое дело браться”
(Жаргон лабухов)
“To think is wrong business to undertake”
Russian slang
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The other extreme - no thinking at all

19.  Our approach to Machine Learning is
 The Golden Mean approach
 Avoid over-theorizing
 Avoid “just code”
– Know what to expect of the solution
– When to apply
– The limitations
– The benefits
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The golden mean
Sages advocate the golden means

20. Types of Machine Learning
 Supervised Machine Learning:
– A model is “trained” with human labeled training data.
– Model then tested on other training data to see performance
– Model can then be applied to unknown data.
– Classification & regression usually supervised.
 Unsupervised Machine Learning
– Model tries to find natural patterns in the data.
– No human input except parameters of the model.
– Example: Clustering
 Semi-Supervised Learning
– Model is trained with a training set which contains mix of trained
and untrained data
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21. Supervised Machine Learning
 Input Data is split into “training” and “test” data, both labeled.
 A Model is trained using training data
 Prediction is made using model.predict()
 Model can be tested using comparing the test dataset
– Mean Squared Error: mean(predicted – actual)
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22. MLLib Algorithm overview
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23. Model Validation
 Models need to be ‘verified’ / ‘validated’
 Split the data set into
– Training set : build / train model
– Test set : validate the model
 Initially 70% training, 30% validation
 Tweak the dials to decrease training and increase validation
 Training set should represent data well-enough
Training Testing
model
23

24. Creating Feature Vectors: Feature Extraction
 Machine Learning only works with vectors. Feature Vectors
are an n-dimensional point in space.
– Select variables from data
– Turn data into numbers (doubles).
– “normalize” (scale down) high magnitude data.
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25. Vectors: Dense versus Sparse
 Dense Vectors
– Usually have a nonzero value for each variable
– The “telecom churn” dataset we use in the labs is a dense dataset.
– Use Vectors.dense
 Sparse Vectors
– Most values are zero (or nonexistent)
– Text Data yields sparse vectors
– One-Hot, factor variables lead to sparse vectors
– Use Vectors.sparse
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26. Creating Vectors From Text
 How to create vectors from text?
– TF/IDF: Term Frequency Inverse Document Frequency
• This essentially means the frequency of a term divided by its
frequency in the larger group of documents (the “corpus”)
• Each word in the corpus is then a “dimension” – you would have
thousands of dimensions.
– Word2Vec
• Another vectorization algorithm
• Uses neural network
• Borders on deep learning
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27. Visualizing Text using WordCloud
State of The Union Speech 2014
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28.  What is deep learning?
– “A neural network with more than 1 hidden layer”
– Deeplearning4j
 But what is a neural network?
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Deep learning

29.  Set of algorithms
 Modeled loosely after the human brain
 Designed to recognize patterns
 Input comes from sensory data
– machine perception
– labeling
– clustering raw input
 Recognized patterns
– Numerical
– Contained in vectors
– Translated from real-world data
• Images
• Sound
• Text
• Time series
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Neural networks

30.  Do I have the data?
 Which outputs do I care about?
– Spam – not spam
– Fraud – not fraud
 Do I have labeled data from which to learn? (Supervised
learning)
 Nah, I just need to group things (Unsupervised learning)
– Normal – anomaly
– Group documents
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Basic steps in a neural network

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Neural network node

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Neural network composition

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Deep neural network

34.  Google
– ParagraphVectors (implemented as doc2vec)
– Represents the meaning of documents
– Based on word2vec and word context
 Facebook
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Deep learning applications

35. ML in Spark
Spark Core
Spark
SQL
Spark
Streaming
ML lib
Standalone YARN MESOS
GraphX
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36. Linear algorithms
Linear algorithms
SVM
Logistic regression
Linear regression

37. Practical use case for SVM
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38. History of logistic regression
 Invented by (Sir) David Cox, UK
 Who wrote 364 books and papers
 Best known for
– Proportional hazards model
– Used in analysis of survival data
– Medical research (cancer)
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39. Classification algorithms
Naïve Bayes
Decision Trees
K-Means

40. Where Naïve Bayes fits in
 There are many classification algorithms in the world
 Naïve Bayes Classifier (NBC) is one of the simplest but most
effective
 K-means and K-nearest neighbors are for numeric data
 But for
– Names
– Symbols
– Emails
– Texts
 NBC may be the best for that
 Bayes can do multiclass (and not only binary) classification
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A is a good candidate for Naïve Bayes
(Credit: Sebastian Raschka)

41. History of Bayes
 Discovered by the Reverend Thomas Bayes (1701–1761)
 Edited and read at the Royal Society by Richard Price (1763)
 Independently reproduced and extended by Laplace (1774)
 Naïve Bayes classifiers studied in 1950’s
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42. Clustering use case
 Anomaly detection
– Find fraud
– Detect network intrusion attack
– Discover problems on servers
– Or on any machinery with sensors
 Clustering does not necessarily detects fraud
– But it points to unusual data
– And the need for further investigation
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43. Network intrusion
 Known unknowns
– Port scanning
– Number of ports accessed per second
– Number of bytes sent/received
 But what about unknown unknowns?
– Biggest thread
– New and as yet unclassified attacks
– Connections that are not knows as attacks
– But are out of the ordinary
– Anomalies that are outside clusters
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44. Shortest Path
 You have a graph (map) of cities
 With distances between them
 Help the mouse find the shortest path to the cheese
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45. Elephant Scale – Big Data Training done right
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