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Engineering Intelligent Systems using Machine Learning

  1. Engineering Intelligent System with Machine Learning Saurabh Kaushik
  2. Agenda Why ML is significant? What is MLTechnology? How to Engineering an Intelligent System? What is Next in MLTechnology? Use Cases & Demo 1 2 3 4 5
  3. Machine Learning vs Traditional Learning
  4. Machine Learning  "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E“ – T. Michell (1997) Example: A program for soccer tactics • Task : Win the game • Performance : Goals • Experience : (x) Players’ movements (y) Evaluation
  5. Why ML is Significant?
  6. Why do Automate? A few thousand years ago: Manual Plowing Today:Automated Plowing Path of Machine Evolution…
  7. Automation Evolution System that Do • Replicate repetitive human actions System that Think • Cognitive capabilities handle judgment-oriented tasks System that Learn/Adapt • Learn to understand context and adapt to users and systemsRobotic Automation CognitiveAutomation IntelligentAutomation Natural Language Processing Big Data Analytics Artificial Intelligence Machine Learning Large Scale Processing Adaptive Alteration Rule Engine Screen Scraping Workflow Unstructured Data Processing (Extraction) Knowledge Modelling (Ontologies) Implementation: • Macro-based applets • Screen Scraping data collection • Workflow Implementation • Process Mapping • Business Process Management Implementation: • Built-in Knowledge repository • Learning capabilities • Ability to work with unstructured data • Pattern recognition • Reading source data manuals Implementation: • Artificial Intelligence Systems • Natural Language Understanding and Generation • Self Optimizing / Self Learning • Predictive Analytics / hypothesis generation • Evidence based learning Capabilities Capabilities Capabilities
  8. Evolution of Machine Intelligence • Raw computing power can automate complex tasks!Great Algorithms + Fast Computers • Automating automobiles into autonomous automata!More Data + Real- Time Processing • Automating question answering and information retrieval!Big Data + In- Memory Clusters • Deep Learning + Smart Algorithms = Master Gamer Deep Learning • New algorithm learns handwriting of unseen symbols from very few training examples (unlike typical Deep Learning) ImproveTraining Efficiency IBM Deep Blue Google Self Driven Cars Watson Jeopardy Deepmind Atari Game One Shot Learning
  9. Why Machine Learning?  Human Behavior & their Life are not logical like Code, not linear like a Formulas and not consistent like Rules, so it is hard for Machines to understand & respond to humans, that is the challenge for todays Digital world. Unless, Machine starts to Learn this ever changing human behavior, it can neither understand effectively nor respond intelligently & personally with its human counterpart. Machine Learning algorithms offers a mechanism to understand this non-linear, non-consistent and intuitive behavior. Formula Behavior Actual Behavior Machine Learning to help machine Learn about Human World.
  10. Where can we Apply?
  11. What is ML Technology?
  12. What is Machine Learning Process ?
  13. Types of Tasks for ML Decide between two classes Group data points tightly Fit the target values Classification Regression Clustering AnomalyDetection Find something out of place Calls to Customer Care Delta Change in Calls Duration Grouping by distance from tower Call drops due to technical issues
  14. How to build Model? Task : Prove Hypothesis Experience : Nature of Training Data Goal : Minimize Loss Function Loss Function = | Predicted Value – Actual Value |
  15. How to evaluate Model Performance? Cross Validation Major Reasons: • Less relevant Feature • Smaller Training Data Set • Higher Polynomials • High/Low Learning Rate • High/Low Regularization Value“Underfitting”
  16. What are Key Data Learning Algorithms? Reinforcement Learning Learning from Data Paradigm • Learning by fully labelled Data • Used For: Prediction, Classification (discrete labels), Regression (real values) • Learning by Data interrelationship • Used for: Clustering, Probability distribution estimation, Finding association (in features) • Learning by Feedback Loop • Used for: Decision making (robot, chess machine) • Learning by partially labelled and Data interrelationships • Used For: Prediction, Classification (discrete labels), Regression (real values)
  17. What are Key Problem Solving Algorithms? ProblemType Paradigm What is probable effect of it? How can we generalize given model? Is this A or B? Is this A or B or C? What is its decision flow/reasoning? Can we draw straight rules from it? How is it Organized? Can combining models gives better output? Classification Algorithms How much/How many it is? Can we get higher abstraction from it? What is common in it? What is the similarity in it? Can it draw finer feature from it? Is it weird? What should I do Next? Anomaly Detection Reinforcement Learning
  18. How to choose amongst algorithms?
  19. How to Engineer an Intelligent System?
  20. Engineering Intelligent System Architecture Build Phase Operation Phase
  21. What is difference between Software vs Intelligent System Engineering? Deployment Monitoring Support Testing Regression/ Integration System Testing NFR / Performance Testing Implementation Code Implementation Unit Testing Designing HLD - Architecture Level LLD – Class and method level System Analysis Requirement Gathering Technical Specification of Requirements Model Deployment Monitoring Evaluating Managing Model Evaluation Error Analysis Tuning Model Model Training Model Selection Model Training Feature Engineering Feature Extraction / Processing Feature Ranking / Selection / Reduction Data Preparation Data Acquisition Data Preprocessing Software System Engineering Process Intelligent System Engineering Process
  23. What is NEXT in ML?  What is DL? • “Deep Learning is a set of algorithms in Machine Learning that Attempts to model high level abstractions in data by using architecture composed of multiple non-linear transformations.” • Deep Learning don’t need to provide explicit Feature Engineering. It learns based on algorithm’s non learner transformation logics.
  24. What is current landscape?
  25. Use case & Demos
  26. Demo – Predicting Consumer Churn  Scenario: • Company has been managing CRM Process for a large US based Telecom giant. • Lately, Client has been showing concerns about Customer churn due to various reasons. • Company wants to help its client by developing an Intelligent System to predict/detect customers which are likely to abandon their subscription. Problem Analysis Data Acquisition Feature Engineering Model Training Model Evaluation State Account Length Area Code Phone Int'l Plan VMail Plan Night Charge Intl Mins Intl Calls Intl Charge CustServ Calls Subscribed (Churn) True/False Predicted Column Hypothesis: • Customer Churning can be predicted by their Usage of Calls as well as Frequency of Customer Care calls. Objective of Demo: • To evaluate and select best performing ML Model for predicting Customer Churn. (Build Phase) Customer Data: Irrelevant Columns Binary Value Columns (Yes/No) Binary Classification Reading CSV File into Data Frame Removing irrelevant columns and modifying data value Train models with three best with Cross Validation Technique Using Confusion Matrix – Find best most suitable Algo
  27. Confusion Matrix Actual Value Predicted Value Correct Value Incorrect Value
  28. Demo - Evaluating Models • Precision -When a classifier predicts an individual will churn, how often does that individual actually churn? (Accuracy) Precision = 235 / 269 Recall = 235 / 483 Precision = 330 / 256 Recall = 330 / 483 Precision = 167 / 211 Recall = 167/ 483 • Recall -When an individual churns, how often does my classifier predict that correctly? (Coverage)
  29. Thank You Saurabh Kaushik