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Primer on Supervised Learning and Unsupervised Learning Modelling for Non-Technical
The document provides an overview of machine learning, including definitions and distinctions between supervised learning, unsupervised learning, and reinforcement learning. It discusses the evolution of artificial intelligence through different waves, emphasizing how machines learn from data and adapt to changing situations. The content also covers various algorithms, neural networks, and learning techniques, aiming to enhance understanding of machine learning applications and future possibilities.
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Primer on Supervised Learning and Unsupervised Learning Modelling for Non-Technical
- 1. Primer On Supervised Learning& UnSupervised Learning Puneet Arora Ecologic Corporation
- 2. About Me linkedin.com/in/puneetarora2000/ ASK QuestionsON twitter.com/puneetarora2000
- 3. Topic Covered: 1) MachineLearning Vocab 2) How machines are made to Learn 3) How we check that machine has learned 4) Future of Machines & Machines ..
- 4. Newton re-discovers Apples ASKQuestions ON twitter.com/puneetarora2000 Newton Computes , Regression Equation
- 5. Newton Apples ASK QuestionsON twitter.com/puneetarora2000 Just a Illustratration
- 6. Newton Apples FallingDown ASK Questions ON twitter.com/puneetarora2000 Just a Illustratration
- 7. Newton Apples FallingDown ASK Questions ON twitter.com/puneetarora2000 9.80665 m/s2 (32.1740 ft/s2 ). Just a Illustr atratio n
- 8. Universal Constants ASK QuestionsON twitter.com/puneetarora2000
- 9. Waves of A.I ASKQuestions ON twitter.com/puneetarora2000
- 10. Types of MLAlgorithms ASK Questions ON twitter.com/puneetarora2000 Types of Supervised Machine Learning Algorithm Probability How Brain Works Brain Learns & Relearns How Nature Solves How Organisms Connect How newton found ‘gravity’
- 11. Waves of A.I ASKQuestions ON twitter.com/puneetarora2000 First-wave, rules-based AI enabled “reasoning over narrowly defined problems” with a reduced level of certainty, like early computer chess matches or tax prep software. rules-based AI = if , then else logic , First Order Logic = Predicate Logic + Forward Chains | Backward Chains
- 12. Reasoning over narrowlydefined problems ASK Questions ON twitter.com/puneetarora2000
- 13. People Are Waitingfor Break through ASK Questions ON twitter.com/puneetarora2000
- 14. Waves of A.I ASKQuestions ON twitter.com/puneetarora2000 Second-wave, or machine-learning AI, is based on “training statistical models on big data,” with minimal capacity for reasoning. Supervised Input Supervised Target Learning
- 15. Waves of A.IASK Questions ON twitter.com/puneetarora2000 Third-wave, or unsupervised-learning AI, is context-aware. Machines with third-wave AI “adapt to changing situations.” UnSupervised Input UnSupervised Target Learning
- 16. Inputs ASK Questions ON twitter.com/puneetarora2000 Images= Colour, Satellite, X-rays, Ultrasound, Thermal, Forensics Colours /Spectrum Properties Morphology / Shape of Objects Statistical Features = Mean, Mode , Median, Skewness etc ..etc Texture Feature : Surface Properties = Coarseness | Smoothness
- 17. Inputs ASK Questions ON twitter.com/puneetarora2000 Audio= Music , Birds Songs , Voice Forensics Voice/Spectrum/Wave/Frequency , FFT Morphology/Segment Size/ Shape of Frames/Segments Statistical Features = Mean, Mode , Median, Skewness etc ..etc Sound Quality :Bass,Timbre Coarseness | Smoothness Sound Quality :Bass,Timbre Coarseness | Smoothness
- 18. Inputs ASK Questions ON twitter.com/puneetarora2000 TextDocuments = Office Files, Forensics Frequency of Words , Sentence length, Paragraph Length , Number of Nouns ,Sequence of Nouns ,Verb, Order of Words, Number of Stop Words ,Stem analysis
- 19. Inputs ASK Questions ON twitter.com/puneetarora2000 TabularData Variables X1, X2 Statistical Features , Domain specific Features Time series Data, Sensor Data
- 20. Outputs ASK Questions ON twitter.com/puneetarora2000 Images,Voice , Audio , Sound , Video, Sensor data Categorical Classification Detection Identification Recognition Recommendation Path Analysis Recognize patterns
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- 23. ASK Questions ON twitter.com/puneetarora2000 ContinuousData is Converted into Categorical Data
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- 26. ASK Questions ON twitter.com/puneetarora2000 ControlStatement = Predicate Logic First Order Logic Still not a Algorithm
- 27. ASK Questions ON twitter.com/puneetarora2000 ControlStatement = Predicate Logic First Order Logic An Algorithm
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- 30. ASK Questions ON twitter.com/puneetarora2000 AnSkeleton of Supervised ML Algorithm
- 31. ASK Questions ON twitter.com/puneetarora2000 Skeletonof Supervised ML Algorithm
- 32. ASK Questions ON twitter.com/puneetarora2000 Lets,Mimic Brain : Neural Network = 1 Unit = Neuron
- 33. ASK Questions ON twitter.com/puneetarora2000 Lets,Mimic Brain : Neural Network = 1 Unit = Neuron
- 34. ASK Questions ON twitter.com/puneetarora2000 Lets,Mimic Brain : Neural Network
- 35. ASK Questions ON twitter.com/puneetaro ra2000 ContemporaryFrameworks Neural Network
- 36. ASK Questions ON twitter.com/puneetaro ra2000 ContemporaryFrameworks Neural Network 'Father of the Pentium Chip'
- 37. ASK Questions ON twitter.com/puneetarora2000 TreeBased Algorithms
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- 40. ASK Questions ON twitter.com/puneetarora2000 HyperparameterTuning Let’s say an ML algorithm has 5 parameters .,DT = 5 How many combinations of the same ML Algorithms can be made Five Factorial = 120 models
- 41. ASK Questions ON twitter.com/puneetarora2000 Skeletonof Unsupervised Learning
- 42. Learning Problems 1. SupervisedLearning 2. Unsupervised Learning 3. Reinforcement Learning (feedback )
- 43. Learning Problems Reinforcement Learning(feedback )
- 44. Hybrid Learning Problems 4.Semi-Supervised Learning 5. Self-Supervised Learning 6. Multi-Instance Learning
- 45. Statistical Inference 7. InductiveLearning 8. Deductive Inference 9. Transductive Learning Learning Techniques
- 46. Learning Techniques (Howalgorithm should learn) 10. Multi-Task Learning 11. Active Learning 12. Online Learning 13. Transfer Learning 14. Ensemble Learning META LEARNING
- 47. Automatically build yourown ML Auto-ML
- 48. Learning Problems 1. SupervisedLearning 2. Unsupervised Learning 3. Reinforcement Learning
- 49. Learning Problems 1. SupervisedLearning 2. Unsupervised Learning 3. Reinforcement Learning
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- 51. ASK Questions ON twitter.com/puneetarora2000 Collaborativework between Humans and Robot (Cobot)
- 52. ASK Questions ON twitter.com/puneetarora2000 SuperSmart Society (Society 5.0)