Machine Learning - Classification Coursera

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. 

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.

• Improve the performance of any model using boosting.

• Scale your methods with stochastic gradient ascent.

• Describe the underlying decision boundaries.  

• Build a classification model to predict sentiment in a product review dataset.  

• Analyze financial data to predict loan defaults.

• Use techniques for handling missing data.

• Evaluate your models using precision-recall metrics.

• Implement these techniques in Python (or in the language of your choice, though Python is highly recommended).

Skills you'll gain

• Machine Learning

• Machine Learning Algorithms

• Algorithms

• Human Learning

• Applied Machine Learning

• Probability & Statistics

• Decision Making

• Python Programming

• Probability Distribution