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Introduction to Unsupervised Learning - Code Heroku
- 1. Please turn offyour webcam If you are joining from a mobile phone be sure to click on Join via Device Audio We are waiting for other participants to join We will begin at 4:30 PM IST
- 2. Mihir Thakkar Founder andInstructor hello@codeheroku.com Introduction to Unsupervised Machine Learning
- 3. SESSION OBJECTIVES ● Quick Recap ●Unsupervised Learning ● Implement a Clustering Algorithm in Python
- 4. Types of MachineLearning Algorithms Supervised Machine Learning House Size (Sq feet) Location Age (years) Prize (Lakh Rs) 500 Mumbai 2 70 1500 Pune 3 100 2000 Banglore 4 60 1000 Mumbai 2 ? 3000 Pune 10 ? Training Data Test Data
- 5. www.codeheroku.com Introduction toUnsupervised Machine Learning Brain scans by functional magnetic resonance imaging. An illustration of finding interesting underlying phenomena in high-dimensional data (Beckmann et al, Phil. Trans. Royal Soc. B, 2005).
- 6. Types of MachineLearning Algorithms Unsupervised Machine Learning Unsupervised Machine Learning Algorithm
- 7. Supervised ML ● LabelledDataset Unsupervised ML ● Prediction/Classification ● More formal problem ● Labels / targets are unknown ● Finding hidden patterns in unlabelled data ● Problem itself is ambiguous
- 8. www.codeheroku.com Introduction toUnsupervised Machine Learning Applications Of Unsupervised ML
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- 13. QUIZ In which ofthese scenarios you would most likely use an unsupervised machine learning algorithm? 1. Given a set of images you are interested in grouping the ones which are similar 2. Given training data about a user’s preferences you are interested in knowing whether they would like/dislike a movie 3. Given a set of 1000 features you are interested in finding features that capture maximum variance in the data
- 14. www.codeheroku.com Introduction toUnsupervised Machine Learning Clustering Movie Time IMDB Rating
- 15. www.codeheroku.com Introduction toUnsupervised Machine Learning Clustering Movie Time IMDB Rating 1. Proximity Measures 1. Evaluation Criteria => What a good cluster looks like?
- 16. www.codeheroku.com Introduction toUnsupervised Machine Learning K-means
- 17. Initialize K centroidsrandomly
- 18. Step 1: Assignment Assigneach data point a cluster based on closest centroid
- 20. Step 2: MoveCentroid Move centroid to location which is the mean of all points in that cluster
- 21. Move centroid tolocation which is the mean of all points in that cluster
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- 26. Quiz The distortion ofa cluster is given by the formula below. Calculate the distortion of a clustering algorithm with following values Data X Data Y Centroid X Centroid Y 5 2 3 3 3 3 2 2 Ans: (5-3)2 + (2-3)2 + 0 + (2-3)2 + (2-3)2Ans: (5-3)2 + (2-3)2 + 0 + (2-3)2 + (2-3)2
- 27. www.codeheroku.com Introduction toUnsupervised Machine Learning Optimal Number of K
- 28. www.codeheroku.com Introduction toUnsupervised Machine Learning Will we always get the best solution?
- 29. www.codeheroku.com Introduction toUnsupervised Machine Learning Seen this before?
- 30. www.codeheroku.com Introduction toUnsupervised Machine Learning Outliers!!
- 31. www.codeheroku.com Introduction toUnsupervised Machine Learning Let’s Build It https://drive.google.com/file/d/1wLwOro1YhfpPr0YqYywx6zLcLn8Ceh30/view?usp=sharing https://github.com/codeheroku/Introduction-to-Machine-Learning/tree/master/
- 32. www.codeheroku.com Introduction toUnsupervised Machine Learning Thank you!
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Editor's Notes
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