COC131 Data Mining, Tuotorials
Weka "The overall goal of our project is to build a state-of-the-art facility for developing machine learning (ML) techniques and to apply them to real-world data mining problems. Our team has incorporated several standard ML techniques into a software "workbench" called WEKA, for Waikato Environment for Knowledge Analysis.

Getting Started with RapidMiner Studio - RapidMiner Documentation
So here you are, a business analyst or developer or jack-of-all-trades. Things are going well, but it's time to fine-tune your business. You've probably collected a myriad of data points about your customer base and would like to determine which customers will remain loyal and which ones will likely churn. Attracting new customers comes at a price, so you'd really like to maintain your current customer base. How do you predict who may leave? What target audiences do you spend marketing dollars on to entice them to stay?

Implementation of k-means Clustering - Edureka
In this blog, you will understand what is K-means clustering and how it can be implemented on the criminal data collected in various US states. The data contains crimes committed like: assault, murder, and rape in arrests per 100,000 residents in each of the 50 US states in 1973. Along with analyzing the data you will also learn about: Finding the optimal number of clusters.Minimizing distortionCreating and analyzing the elbow curve.Understanding the mechanism of k-means algorithm. Let us start with the analysis. The data looks as:
Octave
GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.

Vowpal Wabbit (Fast Learning)
Vowpal Wabbit (Fast Learning) This is a project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm. VW is the essence of speed in machine learning, able to learn from terafeature datasets with ease. Via parallel learning, it can exceed the throughput of any single machine network interface when doing linear learning, a first amongst learning algorithms.
A Guide to Deep Learning by YerevaNN
When you are comfortable with the prerequisites, we suggest four options for studying deep learning. Choose any of them or any combination of them. The number of stars indicates the difficulty. Hugo Larochelle's video course on YouTube.

Machine Learning Software in Java
Tutorial · JohnLangford/vowpal_wabbit Wiki
We did a new version 7.8 tutorial which includes: New tutorials associated with Version 7.4. This includes: A new version 7.0 tutorial is available. It covers the basics and most common options, how to use VW and the data format for different types of problems, such as Binary Classification, Regression, Multiclass Classification, Cost-Sensitive Multiclass Classification, "Offline" Contextual Bandit and Sequence Predictions. Many more advanced options in terms of flags and the data format are not covered.

What is machine learning? Everything you need to know
This device is unable to play the requested video. Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people. From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence -- helping software make sense of the messy and unpredictable real world. But what exactly is machine learning and what is making the current boom in machine learning possible? What is machine learning?

Artelnics
OpenNN is an open source class library written in C++ programming language which implements neural networks, a main area of deep learning research. It is intended for advanced users, with high C++ and machine learning skills. The library implements any number of layers of non-linear processing units for supervised learning. This deep architecture allows the design of neural networks with universal approximation properties.
Top 10 data mining algorithms in plain English
Today, I’m going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper. Once you know what they are, how they work, what they do and where you can find them, my hope is you’ll have this blog post as a springboard to learn even more about data mining. What are we waiting for? Let’s get started! Update 16-May-2015: Thanks to Yuval Merhav and Oliver Keyes for their suggestions which I’ve incorporated into the post.