Machine Learning - Classification

Overview

In this Machine Learning - Classification course offered by Coursera in partnership with University of Washington you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...).  

In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. 

These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks.

Learning Objectives: 

• By the end of this course, you will be able to:

• Describe the input and output of a classification model.

• Tackle both binary and multiclass classification problems.

• Implement a logistic regression model for large-scale classification.  

• Create a non-linear model using decision trees.