In the past few years the generation of data and our capability to store and process it has grown exponentially. There is a need for scalable analytics frameworks and people with the right skills to get the information needed from this Big Data. Apache Mahout is one of the first and most prominent Big Data machine learning platforms. It implements machine learning algorithms on top of distributed processing platforms such as Hadoop and Spark. Starting with the basics of Mahout and machine learning, you will explore prominent algorithms and their implementation in Mahout development. You will learn about Mahout building blocks, addressing feature extraction, reduction and the curse of dimensionality, delving into classification use cases with the random forest and Naïve Bayes classifier and item and user-based recommendation. You will then work with clustering Mahout using the K-means algorithm and implement Mahout without MapReduce. Finish with a flourish by exploring end-to-end use cases on customer analytics and test analytics to get a real-life practical know-how of analytics projects.
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- sheilahong20002015-09-24好书。 有mahout最新的信息。 对我的project很有帮助。
- Qi--Qi2016-05-11比较广泛,还需要多点深度,感谢分享!
- albert6262016-03-30初步了解。。。感谢分享
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