Machine Learning with Mahout

[This blog is from KnowBigData.com. It is pretty old. Many things have changed since then. People have moved to MLLib. We have also moved to CloudxLab.com.]

What is Machine Learning?

Machine Learning is programming computers to optimize a Performance using example data or past experience, it is a branch of Artificial Intelligence.

Types of Machine Learning

Machine learning is broadly categorized into three buckets:

• Supervised Learning – Using Labeled training data, to create a classifier that can predict the output for unseen inputs.
• Unsupervised Learning – Using Unlabeled training data to create a function that can predict the output.
• Semi-Supervised Learning – Make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.

Machine Learning Applications

• Recommend Friends, Dates, Products to end-user.
• Classify content into pre-defined groups.
• Find Similar content based on Object Properties.
• Identify key topics in large Collections of Text.
• Detect Anomalies within given data.
• Ranking Search Results with User Feedback Learning.
• Classifying DNA sequences.
• Sentiment Analysis/ Opinion Mining
• Computer Vision.
• Natural Language Processing,
• BioInformatics.
• Speech and HandWriting Recognition.

Mahout

Mahout – Keeper/Driver of Elephants. Mahout is a Scalable Machine Learning Library built on Hadoop, written in Java and its Driven by Ng et al.’s paper “MapReduce for Machine Learning on Multicore”. Development of Mahout Started as a Lucene sub-project and it became Apache TLP in Apr’10.

• Introduction to Machine Learning and Mahout
• Machine Learning- Types
• Machine Learning- Applications
• Machine Learning- Tools
• Mahout – Recommendation Example
• Mahout – Use Cases
• Mahout Live Example
• Mahout – Other Recommender Algos