Introduction to Machine Learning

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
• 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: ml-summary