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The document is an agenda for a seminar on machine learning techniques and tools. It will cover an introduction to machine learning, common techniques like classification, clustering and regression. It will also discuss tools for machine learning like Apache Mahout, Weka, Spark MLLib and R. Finally, it will include a hands-on demonstration of machine learning algorithms and discuss benefits of using machine learning.

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Data Science Company Machine Learning in Practice An InfoFarm Seminar Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Data Science Big Data Identifying, extracting and using data of all types and origins; exploring, correlating and using it in new and innovative ways in order to extract meaning and business value from it.
2 Data Scientists 4 Big Data Consultants 1 Infrastructure Specialist Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Java PHP E-Commerce Mobile Web Development
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Agenda • 13:00 What is Machine Learning? • 13:30 Techniques • 14:30 Tools • 15:00 Practical examples • 16:00 Wrap up Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
What is Machine Learning? Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Magic?
Machine Learning is a subfield of computer science and statistics that deals with systems that can learn from data, instead of follow explicitly programmed instructions. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Machine Learning vs Data Science vs Big Data • You don’t need Big Data to leverage the benefits of machine learning, but more learning data makes a better machine • Data Science can help you to get the most out of Machine Learning • Machine Learning can help you to get the most out of Data Science Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Terminology Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Terminology Weight (g) Wingspan (cm) Webbed feet? Back color Species 1000.1 125.0 No Brown Buteo jamaicenis 3000.7 200.0 No Gray Sagittarius serpentarius 3300.0 220.3 No Gray Sagittarius serpentarius 4100.0 136.0 Yes Black Gavia Immer 3.0 11.0 No Green Colothorax lucifer 570.0 75.0 No Black Campephilus principalic • Features / attributes • Instance / data point • Label / target variable • Factorial versus Numeric versus Binary data
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Learning • Supervised Learning • Unsupervised Learning
Techniques Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
Machine Learning Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Clustering Classification Association Rules Regression Information extraction
Classification • Predict a category for a given instance • Mostly supervised learning. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Algorithms – Naïve Bayes – Support Vector Machine – Decision Trees – Neural Networks
Classification: Use Cases • Incoming mail redirection • Sentiment analysis • Order picking optimization Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Clustering • Try to find clusters in unstructured data Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Unsupervised learning • Algorithms: K-Means
Clustering: Use cases • Customer profiling • Grouping of shopping items • Recommendation systems • Fraud detection Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Association Rule Learning • Find interesting relations • Find frequent occurring patterns Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Algorithms – Apriori – Singular Value Decomposition – FP-growth
Association Rule Learning: Use Cases • Recommendations Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Data exploration • Find connections between unrelated events • Frequent pattern mining
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Regression • Prediction of a quantity • Algorithms: – Linear regression – Logistic regression
Regression: Use Cases • Order Quantity Prediction Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Lag analysis • Trend estimation
Information Extraction • Extract variables out of unstructured data like text. • Named Entity Extraction Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye Tools
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Apache Mahout Pro Contra Relatively stable Poor documentation Build on Hadoop – Scales well Mahout is currently migrating from Apache Hadoop to Apache Spark. Development is slow and Apache Spark already built a machine learning library of their own… Instant legacy? Command-line access for most algorithms Kind of slow for smaller use cases All important algorithms are available
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Weka Pro Contra A lot of algorithms are available Not ‘Big Data’ ready Graphical user interface for prototyping and experimenting Requires custom data format – ARRF-files Available as a Java library Optimized for academic use cases
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Apache Spark: MLLib Pro Contra Based on Apache Spark – Very, fast and scalable Based on Apache Spark – Requires knowledge of Spark and Scala Very fast development cycle, new features are rolling out every couple of months Relatively new, so a small choice of algorithms. But the essential ones are there. New and refreshing API, easy integration with other components of Apache Spark.
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be R Pro Contra A lot of algorithms are available Can run on Hadoop/Spark, but requires a lot of knowledge from both platforms Well documented Must learn a new language Lot’s of existing packages, that are easily available
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Noteworthy Java • DeepLearning4J • Mallet • MOA Python • NLTK • Theano • PyBrain • SciKit-Learn Lua • Torch General • LibSVM • LibLinear
Integration with Software Development Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Development Cycle Collect Analyze Extract Train Test Use
Feature extraction • Describe an instance to be used in an algorithm • Recognize hand-written digits by converting the images to lines of 1’s and 0’s 00000000000001000000000000000000 00000000000001110000000000000000 00000000000011110000000000000000 00000000001111100000000000000000 00000000001111000000000000000000 00000000000111100000000000000000 00000000001111100000000000000000 00000000011111000000000000000000 00000000011110000000000000000000 00000000111110000000000000000000 00000000011111000000000000000000 00000000111111000000000000000000 00000000111110000000000000000000 00000000111100000000000000000000 00000000011110000000000000000000 00000000111110000111000000000000 00000001111111111111111100000000 00000001111111111111111110000000 00000001111111111111111110000000 00000000111111111111111111100000 00000001111111110000011111100000 00000001111100000000000111100000 00000000111100000000000111100000 00000000011110000000000011110000 00000000011111000000000011110000 00000000011111100000001111110000 00000000011111111111111111110000 00000000011111111111111111100000 00000000000111111111111111100000 00000000000011111111111111100000 00000000000000111111111000000000 00000000000000001111110000000000 Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Training an algorithm 1. Collect you’re data as a collection of Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be instances 2. Split you’re data set into a training set and a testing set 3. Train the algorithm with the training set 4. Validate the results using the test set
Runtime model • During training most algorithms generate a mathematical runtime model. • Model should be updated on a regular basis Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
A / B Testing • Slow integration in the main system. • If the machine is certain (enough) the machine can take over Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Hands-on Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
Demo • K-Nearest Neighbour Classifier Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Clustering using Weka • Named-Entity Extraction • Classification of tweets
What’s in it for you? Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
Benefits of using machine learning • Automate repetitive tasks • Can be a solution for problems that are difficult to automate • Gain insights about your business • Optimize business decisions by using the opinion of the computer Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
Questions? Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
Wrap-up Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye

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Machine learning

  • 1. Data Science Company Machine Learning in Practice An InfoFarm Seminar Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 2. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Data Science Big Data Identifying, extracting and using data of all types and origins; exploring, correlating and using it in new and innovative ways in order to extract meaning and business value from it.
  • 3. 2 Data Scientists 4 Big Data Consultants 1 Infrastructure Specialist Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 4. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Java PHP E-Commerce Mobile Web Development
  • 5. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 6. Agenda • 13:00 What is Machine Learning? • 13:30 Techniques • 14:30 Tools • 15:00 Practical examples • 16:00 Wrap up Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 7. What is Machine Learning? Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  • 8. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Magic?
  • 9. Machine Learning is a subfield of computer science and statistics that deals with systems that can learn from data, instead of follow explicitly programmed instructions. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 10. Machine Learning vs Data Science vs Big Data • You don’t need Big Data to leverage the benefits of machine learning, but more learning data makes a better machine • Data Science can help you to get the most out of Machine Learning • Machine Learning can help you to get the most out of Data Science Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 11. Terminology Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  • 12. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Terminology Weight (g) Wingspan (cm) Webbed feet? Back color Species 1000.1 125.0 No Brown Buteo jamaicenis 3000.7 200.0 No Gray Sagittarius serpentarius 3300.0 220.3 No Gray Sagittarius serpentarius 4100.0 136.0 Yes Black Gavia Immer 3.0 11.0 No Green Colothorax lucifer 570.0 75.0 No Black Campephilus principalic • Features / attributes • Instance / data point • Label / target variable • Factorial versus Numeric versus Binary data
  • 13. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Learning • Supervised Learning • Unsupervised Learning
  • 14. Techniques Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  • 15. Machine Learning Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Clustering Classification Association Rules Regression Information extraction
  • 16. Classification • Predict a category for a given instance • Mostly supervised learning. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Algorithms – Naïve Bayes – Support Vector Machine – Decision Trees – Neural Networks
  • 17. Classification: Use Cases • Incoming mail redirection • Sentiment analysis • Order picking optimization Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 18. Clustering • Try to find clusters in unstructured data Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Unsupervised learning • Algorithms: K-Means
  • 19. Clustering: Use cases • Customer profiling • Grouping of shopping items • Recommendation systems • Fraud detection Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 20. Association Rule Learning • Find interesting relations • Find frequent occurring patterns Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Algorithms – Apriori – Singular Value Decomposition – FP-growth
  • 21. Association Rule Learning: Use Cases • Recommendations Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Data exploration • Find connections between unrelated events • Frequent pattern mining
  • 22. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Regression • Prediction of a quantity • Algorithms: – Linear regression – Logistic regression
  • 23. Regression: Use Cases • Order Quantity Prediction Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Lag analysis • Trend estimation
  • 24. Information Extraction • Extract variables out of unstructured data like text. • Named Entity Extraction Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 25. Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye Tools
  • 26. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 27. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Apache Mahout Pro Contra Relatively stable Poor documentation Build on Hadoop – Scales well Mahout is currently migrating from Apache Hadoop to Apache Spark. Development is slow and Apache Spark already built a machine learning library of their own… Instant legacy? Command-line access for most algorithms Kind of slow for smaller use cases All important algorithms are available
  • 28. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 29. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Weka Pro Contra A lot of algorithms are available Not ‘Big Data’ ready Graphical user interface for prototyping and experimenting Requires custom data format – ARRF-files Available as a Java library Optimized for academic use cases
  • 30. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 31. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Apache Spark: MLLib Pro Contra Based on Apache Spark – Very, fast and scalable Based on Apache Spark – Requires knowledge of Spark and Scala Very fast development cycle, new features are rolling out every couple of months Relatively new, so a small choice of algorithms. But the essential ones are there. New and refreshing API, easy integration with other components of Apache Spark.
  • 32. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 33. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be R Pro Contra A lot of algorithms are available Can run on Hadoop/Spark, but requires a lot of knowledge from both platforms Well documented Must learn a new language Lot’s of existing packages, that are easily available
  • 34. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Noteworthy Java • DeepLearning4J • Mallet • MOA Python • NLTK • Theano • PyBrain • SciKit-Learn Lua • Torch General • LibSVM • LibLinear
  • 35. Integration with Software Development Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  • 36. Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be Development Cycle Collect Analyze Extract Train Test Use
  • 37. Feature extraction • Describe an instance to be used in an algorithm • Recognize hand-written digits by converting the images to lines of 1’s and 0’s 00000000000001000000000000000000 00000000000001110000000000000000 00000000000011110000000000000000 00000000001111100000000000000000 00000000001111000000000000000000 00000000000111100000000000000000 00000000001111100000000000000000 00000000011111000000000000000000 00000000011110000000000000000000 00000000111110000000000000000000 00000000011111000000000000000000 00000000111111000000000000000000 00000000111110000000000000000000 00000000111100000000000000000000 00000000011110000000000000000000 00000000111110000111000000000000 00000001111111111111111100000000 00000001111111111111111110000000 00000001111111111111111110000000 00000000111111111111111111100000 00000001111111110000011111100000 00000001111100000000000111100000 00000000111100000000000111100000 00000000011110000000000011110000 00000000011111000000000011110000 00000000011111100000001111110000 00000000011111111111111111110000 00000000011111111111111111100000 00000000000111111111111111100000 00000000000011111111111111100000 00000000000000111111111000000000 00000000000000001111110000000000 Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 38. Training an algorithm 1. Collect you’re data as a collection of Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be instances 2. Split you’re data set into a training set and a testing set 3. Train the algorithm with the training set 4. Validate the results using the test set
  • 39. Runtime model • During training most algorithms generate a mathematical runtime model. • Model should be updated on a regular basis Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 40. A / B Testing • Slow integration in the main system. • If the machine is certain (enough) the machine can take over Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 41. Hands-on Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  • 42. Demo • K-Nearest Neighbour Classifier Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be • Clustering using Weka • Named-Entity Extraction • Classification of tweets
  • 43. What’s in it for you? Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  • 44. Benefits of using machine learning • Automate repetitive tasks • Can be a solution for problems that are difficult to automate • Gain insights about your business • Optimize business decisions by using the opinion of the computer Veldkant 33A, Kontich ● info@infofarm.be ● www.infofarm.be
  • 45. Questions? Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye
  • 46. Wrap-up Veldkant 33A, Kontich ● info@infofarmDa.btae S●ciwewncwe. inCfoomfaprman.bye