- Big Data Analytics Tutorial
- Big Data Analytics - Home
- Big Data Analytics - Overview
- Big Data Analytics - Data Life Cycle
- Big Data Analytics - Methodology
- Core Deliverables
- Key Stakeholders
- Big Data Analytics - Data Analyst
- Big Data Analytics - Data Scientist
- Big Data Analytics Project
- Data Analytics - Problem Definition
- Big Data Analytics - Data Collection
- Big Data Analytics - Cleansing data
- Big Data Analytics - Summarizing
- Big Data Analytics - Data Exploration
- Data Visualization
- Big Data Analytics Methods
- Big Data Analytics - Introduction to R
- Data Analytics - Introduction to SQL
- Big Data Analytics - Charts & Graphs
- Big Data Analytics - Data Tools
- Data Analytics - Statistical Methods
- Advanced Methods
- Machine Learning for Data Analysis
- Naive Bayes Classifier
- K-Means Clustering
- Association Rules
- Big Data Analytics - Decision Trees
- Logistic Regression
- Big Data Analytics - Time Series
- Big Data Analytics - Text Analytics
- Big Data Analytics - Online Learning
- Big Data Analytics Useful Resources
- Big Data Analytics - Quick Guide
- Big Data Analytics - Resources
- Big Data Analytics - Discussion
Machine Learning for Data Analysis
Machine learning is a subfield of computer science that deals with tasks such as pattern recognition, computer vision, speech recognition, text analytics and has a strong link with statistics and mathematical optimization. Applications include the development of search engines, spam filtering, Optical Character Recognition (OCR) among others. The boundaries between data mining, pattern recognition and the field of statistical learning are not clear and basically all refer to similar problems.
Machine learning can be divided in two types of task −
- Supervised Learning
- Unsupervised Learning
Supervised Learning
Supervised learning refers to a type of problem where there is an input data defined as a matrix X and we are interested in predicting a response y. Where X = {x1, x2, …, xn} has n predictors and has two values y = {c1, c2}.
An example application would be to predict the probability of a web user to click on ads using demographic features as predictors. This is often called to predict the click through rate (CTR). Then y = {click, doesn’t − click} and the predictors could be the used IP address, the day he entered the site, the user’s city, country among other features that could be available.
Unsupervised Learning
Unsupervised learning deals with the problem of finding groups that are similar within each other without having a class to learn from. There are several approaches to the task of learning a mapping from predictors to finding groups that share similar instances in each group and are different with each other.
An example application of unsupervised learning is customer segmentation. For example, in the telecommunications industry a common task is to segment users according to the usage they give to the phone. This would allow the marketing department to target each group with a different product.