MACHINE LEARNING 101 AGENDA o What is Machine

MACHINE LEARNING 101

AGENDA o What is Machine Learning? o Machine Learning Algorithms o Machine Learning Process o Demo

ML ?

ML ?

Machine learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make an inference about something in the world - Nvidia

Learn from Experience MACHINE LEARNING Learn from Experience Follow Instructions

TYPES OF ML

TYPES OF ML • SUPERVISED LEARNING • UNSUPERVISED LEARNING • REINFORCEMENT LEARNING

SUPERVISED LEARNING • Takes data that already has an answer (labeled data) • For an example: o Classify images that has oranges in it o We need images that have already been labeled as an orange or not an orange • Its supervised because we can tell the model what it got wrong

Training Dataset Image Label Testing Dataset Image Label True ? False ?

SUPERVISED LEARNING • Takes data that already has an answer (labeled data) • For an example: o Classify images that has oranges in it o We need images that have already been labeled as an orange or not an orange • Its supervised because we can tell the model what it got wrong

ALGORITHMS Supervised learning

CLASSIFICATION ALGORITHMS • Classification is about predicting labels • Examples: § Is this an orange? § Is this an apple, orange or lime? • Classification algorithms: § Linear Classifiers § Support Vector Machines § Decision Trees § Boosted Trees § Random Forest

REGRESSION ALGORITHMS • Regression is about predicting quantity • Examples: § How much is the house? § What is the salary? § What is the end of the day balance? • Regression algorithms: § Linear Regression § Logistic Regression § Decision Trees § Naïve Bayes Classification

ML PROCESS

DEMO

QUESTIONS?

EXTRA

UNSUPERVISED LEARNING • No supervision • Takes data that has not been labeled • Based on the hidden patterns and structures of the data, it creates clusters • Its unsupervised because there's no way of telling the model what's wrong or right and we let the model do its own thing

UNSUPERVISED LEARNING • No supervision • Takes data that has not been labeled • Based on the hidden patterns and structures of the data, it creates clusters • Its unsupervised because there's no way of telling the model what's wrong or right and we let the model do its own thing

REINFORCEMENT LEARNING • It involves an autonomous agent learning how to navigate an uncertain environment with the goal of maximizing a numerical reward • Examples: • Self driving cars • Google Deep Mind’s Alpha. Zero was able master the games of chess, shogi (Japanese chess), and Go, and beat world-champion in each case

Reference • Machine Learning For Beginners From Zero Level https: //blog. usejournal. com/machine-learningfor-beginners-from-zero-level-8 be 5 b 89 bf 77 c • A Friendly Introduction to Machine Learning https: //www. youtube. com/watch? v=Ip. Gx. LWOI Zy 4&list=PLEr 9 REFg. Kb. N 0 Ja. Iv. O 1 fl. OCD 450 XO 6 sat&index=5&t=0 s • What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning? https: //blogs. nvidia. com/blog/2016/07/29/wha ts-difference-artificial-intelligence-machinelearning-deep-learning-ai/ • Fusion Alliance Machine Learning 101 Deck