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.
- Section 3: Ensemble Methods (Bagging, Random Forest, Boosting) and Clustering (HAC, KMeans, GMM, Spectral Clustering).
- Section 4: Dimension Reduction, Major problems in Machine Learning, ML libraries and Summaries.
You can download the whole article of summarizing Machine Learning at here: https://ducminhkhoi.github.io/Machine-Learning-Tutorials/ml-summary.pdf