This is the overview of basic and important machine learning models, methods and concepts and theories. I acknowledge all information and knowledge including images, data… I have taken from those two courses: https://www.coursera.org/learn/machine-learning and http://classes.engr.oregonstate.edu/eecs/fall2015/cs534/.
Our series comprise of following topics:
Section 1: Introduction, Linear regression, Generative and Discriminative Model, Perceptron, Logistic Regression, Naive Bayes and Gaussian Discriminant Analysis (this post). Section 2: Four important Discriminative Models: K-Nearest Neighbors, Support Vector Machine, Decision Tree and Neural Network.
This is my first post on Machine Learning, Deep Learning and Computer Vision series in Medium. I am currently a Ph.D. Student in Computer Science with research interests are Computer Vision and Machine Learning. On this series, I will share with you the roadmap I have experienced. I hope that everything I share is somehow helps you save time when exploring Machine Learning field.
First, I will discuss about the relationship between Machine Learning and other areas.