Regression
In regression, the target variable is continuous or ordered whole values
For example, suppose you are working on stock market prediction, and you would like to predict the price of a particular stock tomorrow (measured in dollars). This is a regression problem because the target variable (stock price) is continuous.
To solve a regression problem, we typically use supervised learning algorithms.
Classification
In classification, the target variable is categorical and unordered.
For example, classifying tweets into positive, negative and natural is a classification problem.
To solve a classification problem, we typically use supervised learning algorithms. We have labels for some points, and we want a ‘rule’ that will accurately assign labels to new points.
Clustering
In clustering, you group (cluster) the data into some number of groups (clusters) without labels.
For example, segmenting customer database based on similar buying patterns is a clustering problem.
To solve a clustering problem, we typically use unsupervised learning algorithms. We group points into clusters based on how ‘near’ they are to on another by identifying structure in data.

1 The 3rd Eye for Your Car

2 A few UW students hacked the Google Perspective API

3 A Complete List of Free Dev Resources Exclusive to Students and Educators

4 Microsoft Azure Machine Learning Cheat Sheet v6 – Released today

5 Interesting Visual Explaining Machine Learning to Beginners

6 New Book: Machine Learning Projects for .NET Developers

7 Best Machine Learning & AI Cloud Services in the Market

8 ML101: How to Choose a Machine Learning Algorithm for Multiclass Classification Problems

9 ML101: How to Choose Machine Learning Algorithms

10 ML101: How to Choose a Machine Learning Algorithm for Twoclass Classification Problems
Pingback: ML101: How to Choose a Machine Learning Algorithm for Regression Problems @ Scott Ge
Pingback: ML101: How to Choose a Machine Learning Algorithm for Twoclass Classification Problems @ Scott Ge
Pingback: ML101: How to Choose Machine Learning Algorithms @ Scott Ge
Pingback: ML101: How to Choose a Machine Learning Algorithm for Multiclass Classification Problems @ Scott Ge