Summary Introduction to Machine Learning: Foundations and Applications

Introduction to Machine Learning Algorithms




Gradient Boosting for Competitive Predictions


 Gradient Boosting is a popular machine learning algorithm used for
making predictions
 It works by building an ensemble of weak prediction models, typically
decision trees
 The algorithm sequentially trains new models to correct the errors made
by the previous models

Classification: Logistic Regression and K Nearest Neighbors


 Logistic Regression is a linear model used for classification tasks
 It works by estimating the probability of a data point belonging to a
certain class
 K Nearest Neighbors (KNN) is a simple, instance-based learning
algorithm used for classification
 It works by finding the 'k' nearest data points to a given data point and
then assigning the majority class

Unsupervised Learning: Clustering and Collaborative Filtering


 Clustering is a type of unsupervised learning that involves grouping data
points together based on similarities
 Collaborative Filtering is a technique used for making recommendations
by finding patterns in user behavior

Decision Trees: How to Make Predictions using Decision Trees


 Decision Trees are a type of supervised learning algorithm used for both
classification and regression tasks