Machine Learning - Entering into The Wonderful Galaxy of Machine Learning
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This document provides an overview of machine learning. It discusses what machine learning is, the different types including supervised vs. unsupervised learning and regression vs. classification problems. It also summarizes several common machine learning techniques like linear regression, Naive Bayes, k-means clustering, decision trees, random forests, AdaBoost, support vector machines, recurrent neural networks, and convolutional neural networks. The document aims to introduce readers to the "wonderful galaxy of machine learning."
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- 1. Machine Learning Entering into The Wonderful Galaxy of Machine Learning Version: 0.1 Minhas Kamal Software Engineer, SRBD, Dhaka, Bangladesh Website: minhaskamal.github.io Date: 28th Nov, 2017
- 2. We Will Learn ‒ What Is ML? ‒ Types of ML ‒ Some ML Techniques ‒ Deep Learning! TotalSlides-11
- 3. 01 Machine Learning a field of computer science that gives computers the ability to learn without being explicitly programmed. - Wikipedia
- 8. 01 Machine Learning People worry that computers will get too smart and take over the world, but the real problem is that they're too stupid and they've already taken over the world.“
- 9. 02 Types of ML: Feedback System
- 10. 02 Types of ML: Feedback System UnsupervisedSupervised
- 11. 03 Types of ML: Nature of Output
- 12. 03 Types of ML: Nature of Output RegressionClassification
- 15. 04 Linear Regression Y` = m * X + c e = Y – Y` m = m - e c = c - e
- 17. 05 Naïve Bayes
- 18. 05 Naïve Bayes P(h|d) = ------------- P(h) * P(d|h) P(d)
- 19. 05 Naïve Bayes
- 20. 05 Naïve Bayes
- 21. 05 Naïve Bayes
- 22. 05 Naïve Bayes
- 23. INTERMISSION
- 25. 06 K-Means Clustering μ = ------ ∑xi n d = μ - xi
- 28. 07 Decision Tree
- 29. 07 Decision Tree
- 30. 07 Random Forest
- 31. 08 Adaboost
- 37. Next We Explore The Brilliant World of Deep-Learning