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1 machine learning demystified
This document provides an overview of machine learning, including its definitions, applications across various industries, and the necessary prerequisites to start learning. It categorizes algorithms based on different criteria such as learning style and function type, and discusses popular tools and frameworks used in machine learning. The growth of this field is attributed to advancements in data capture, computing power, and demand for automation.
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1 machine learning demystified
- 1. Dr Nisha Arora MachineLearning Demystified Image credit - Google Images
- 2. Contents 2 What isMachine Learning? Machine Learning Applications Pre-requisites for ML Learning ML ML Algorithm Categorization Supervised, Un-supervised & Semi-supervised Algorithms Parametric & Non-Parametric Algorithms The ML Framework Machine Learning Tools R Vs Python Vs SAS Reference & Resources
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- 8. “Machine learningis the next Internet” (Tony Tether, Director, DARPA) “Machine learning is the hot new thing” (John Hennessy, President, Stanford) “Web rankings today are mostly a matter of machine learning” (Prabhakar Raghavan, Dir. Research, Yahoo) “Machine learning is today’s discontinuity” (Jerry Yang, CEO, Yahoo) Quotes
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- 10. 10 Machine Learning isthe Study of algorithms that Improve their performance At some task With experience Machine learning is the science of getting computers to act without being explicitly programmed – Andrew Ng Machine Learning is the training of a model from data that generalizes a decision against a performance measure – Jason in his blog What is Machine Learning?
- 11. 11 The fieldof machine learning is concerned with the question of how to construct computer programs that automatically improve with experience - Tom Mitchell in his book Machine Learning Vast amounts of data are being generated in many fields, and the statisticians’s job is to make sense of it all: to extract important patterns and trends, and to understand “what the data says”. We call this learning from data - Hastie, Tibshirani & Friedman Other Definitions
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- 13. 13 Machine Learning Applications Finance Credit Scoring Fraud Detection Retail Market Basket Analysis Customer Relationship Management (CRM) Marketing Customer Segmentation Customer churn prediction
- 14. 14 Machine Learning Applications Human Resource management Internal mobility (Matching of employees and job descriptions) External recruitment (Browsing candidates through social platforms like linkedIn) Skills management: mapping all company skills (including their relationships e.g. one programming language being close to another one)
- 15. 15 Machine Learning Applications Automating employee access control by Amazon To develop a computer algorithm that will predict which employees should be granted access to what resources. To identify whales in the ocean based on audio recordings by Cornell University so that ships can avoid hitting them To determine which bird species is/are on a given audio recording collected in field conditions by Oregon State University Identifying heart failure by IBM researchers A way to extract heart failure diagnosis criteria from free-text physician notes.
- 16. 16 Machine Learning Applications To predict whether someone is a psychopath based on his twitter usage To predict in advance whether a product launch will be successful or not. Stop malware Self driving cars Automatic speech recognition, automatic voice/face/finger print recognition, automatic medical diagnostics and many more
- 17. Growth of MachineLearning The rise and shine of machine learning is due to Improved data capture, networking, faster computers New sensors / Input Output devices Demand for self-customization to user, environment Improved machine learning algorithms
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- 20. 20 Programming Mathematics/ Numerical Optimization LinearAlgebra/ Calculus Introductory Statistics Prerequisites
- 21. 21 Do you reallyneed to be an expert in math? The real prerequisite for machine learning isn’t math, it’s data analysis https://www.r-bloggers.com/the-real-prerequisite-for-machine- learning-isnt-math-its-data-analysis/ http://courses.washington.edu/css490/2012.Winter/lecture_slide s/02_math_essentials.pdf
- 22. 22 Do you reallyneed to be an expert in programming? You do not have to be an excellent programmer to start your career in machine learning http://machinelearningmastery.com/what-if-im-not-a-good- programmer/
- 23. 23 Adapt, improvise andconquer Finish what you start no matter what Start even when you are not ready Passion for machine learning Prerequisites https://www.quora.com/What-are-prerequisites-to-start-learning-Machine-Learning
- 24. 24 Learning ML Adjust Mindset (believe!) Picka Process Pick a Tool Practice on Datasets Build a Portfolio http://machinelearningmastery.com/machine-learning-mastery-method/
- 25. 25 ML Algorithm Categorization Onthe basis of learning style Supervised Learning • Regression, • Logistic regression • Back propagation neural network Unsupervised Learning • K- means clustering • Association rules Semi- supervised Learning • Regression • Classification
- 26. 26 Image Credit:Pinterest
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- 33. 33 ML Algorithm Categorization Onthe basis of similarity in form/function Regression Classification Clustering Anomaly Detection Recommender System Regularization Tree Based Algo ANN Deep Learning
- 34. 34 ML Algorithm Categorization Onthe basis of similarity in form/function, there are many other algorithms such as ensemble methods, reinforcement learning, computer vision, natural language processing (NLP), dimensionality reduction algorithms, Baysian algorithms, & Instance based algorithms etc. Read more: http://www.cs.uvm.edu/~icdm/algorithms/index.shtml
- 35. 35 ML Algorithm Categorization Onthe basis of target function Parametric Algorithms • Algorithms that simplify the function to a known form • E.g., Linear Reg, Logistic Reg. Non-Parametric Algorithms • Algorithms that don’t make strong assumptions about the form of the function • E.g., SVM, ANN, Decision Trees
- 36. 36 Parametric Vs Non-ParametricAlgorithms Parametric Algorithms Simple Speed Less data Non-Parametric Algorithms Difficult to interpret Slower More data
- 37. 37 Parametric Vs Non-ParametricAlgorithms Parametric Algorithms Constrained Limited Complexity Poor fit Under-fitting Non-Parametric Algorithms Power Flexible Performance Over-fitting Trade-off between prediction accuracy & model interpretability
- 38. 38 Data is likepeople – Interrogate it hard enough and it will tell you whatever you want to hear Just like that
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- 40. The machine learningframework y = f (x) Training Given a training set of labeled examples {(x1,y1), …, (xN, yN)}, estimate the prediction function f by minimizing the prediction error on the training set Testing Apply f to a never before seen test example x and output the predicted value y = f(x) output prediction function Input
- 41. Training Labels Training Images Training Image Features Learned model The ML framework- Training Classification Example
- 42. The ML framework- Testing Prediction- Apple Image Features Test Image Learned model Prediction - Orange Image Features Test Image Learned model Prediction- Cherry Image Features Test Image Learned model
- 43. How machine learningalgorithms works? Model Representation Model Evaluation Model Improvement
- 44. 44 Machine Learning Tools/Libraries R– e1071, randomForest, caret, glmnet, nnet, tree, RWeka Python – Skitlearn, PyBrain, NumPy, SciPy, matplotlib, pandas Hadoop – Mahout, Spark, Weka RapidMiner Orange KNIME H2O Weka Java Machine Learning Library
- 45. ML in Practice Understanding domain, prior knowledge, and goals Data integration, selection, cleaning, pre-processing, etc. Learning models Interpreting results Consolidating and deploying discovered knowledge Loop
- 46. References 46 Elements ofStatistical Learning by Hastie, Tibshirani, Friedman Foundations of Machine Learning by Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar Pattern Recognition and Machine Learning by Bishop Machine Learning with R by Brett Lantz's Machine Learning for Hackers by Conway & White
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