The Online course of Mahout by Shiksha Tarang, which helps in expertising, the basic features relating to machine learning and to fit Mahout in the Hadoop ecosystem. This gives a mixture of knowledge linking to Mahout on Hadoop, recommendation systems, learning machine techniques.
Along with the analysis of the massive skill techniques relating to various levels long with data scaling on and off over cloud, proficiency with complex ecosystem, parallel algorithm etc.,
Course Objective:
After the successful completion of the course the individual professionalise in the following:
On the whole helps in gaining the awareness over all the machine learning techniques.
Who can Learn this course?
Any technical graduate is eligible for taking up this online course of Mahout.
The individuals interested growing towards the Big Data Technology can also take up this course.
Prerequisites
For the beginners having mathematical knowledge and additionally having knowledge on java development can help the individual to get groomed into the profile of Mahout.
Recommended to have knowledge on Hadoop and the basic java, where the primary concepts are dealt during the course.
The learning of this particular course is similar to the Hadoop frameworks and ecosystem Components.
Why Learn Mahout?
The promising nature of the Machine Learning and Apache Mahout lies in, bringing all the data that is present in the system in a random manner and to arrange them in a systematic manner. This data can be used for the processing of hundreds and thousands of professional e-mails messages on a day to day basis, it can also be a user driven information relating to petabytes of weblogs. This particular tool is used to rearrange all the enriched data that was great.
The course curriculum is designed by highly professional and expertise tutors who wish to deliver conceptual training. This course focuses on the in-depth knowledge of very module with the practical presentation by the experts.
Course Curriculum
a. Collaborative
b. User based
c. Item based
8. Case Studies with real time experiences:
Not reviewed yet