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Introduction to Mahout

Last updated on Nov 15,2022 2.9K Views

IST: 08:30 PM – 09:30 PM, 10th October’14

PDT: 08:00 AM – 09:00 AM, 10th October’14

Limited seats!! Fill in the form on the right and book your slot today.

Hi all, we are conducting a Free Webinar on Apache Mahout. The title of the webinar is ‘Introduction to Mahout’. Essential topics will be discussed in detail in the webinar. Any queries or doubts can be clarified during the session.

What is Mahout?

Apache Mahout, a project developed by Apache Software Foundation, is meant for Machine Learning. It enables machines learn without being overtly programmed. It produces scalable machine learning algorithms, extracts recommendations and relationships from data sets in a simplified way. Apache Mahout is an open-source project, which is free to use under the Apache license. It runs on Hadoop, using the MapReduce paradigm. With its data science tools, Mahout enables:

  • Collaborative Filtering
  • Clustering
  • Classification
  • Frequent Itemset Mining

Why should you learn Apache Mahout?

  1. Turns big data into useful information in a faster and easier way leveraging your business capabilities
  2. Runs on Hadoop, making it easier to work on for those who know Hadoop
  3. Being integrated with Hadoop/HDFS, Mahout implements distributed memory algorithms that can be applied to data sets much larger than any other technique can handle.
  4. One of the hot machine learning projects that are being widely used by organizations worldwide

Topics to be covered in the Webinar

Learn Machine Learning with Mahout and why Mahout?
  1. Learn Machine Learning with Mahout and why Mahout?
  2. Understand clustering and K-means Clustering
  3. Understand Mahout and Hadoop
  4. Learn about Mahout Job Trends
  5. Learn Classification on Twitter Data

Once you register, you will get a confirmation mail with details regarding the schedule and the process to join the webinar.

Also, check out our Free Webinar on ‘Introduction to PMP’. Click here to know more!

Learn more about Apache Mahout>>


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Introduction to Mahout