Online or onsite, instructor-led live Apache Spark MLlib training courses demonstrate through interactive discussion and hands-on practice the fundamentals and advanced topics of Apache Spark MLlib.
Apache Spark MLlib training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Apache Spark MLlib training can be carried out locally on customer premises in Nagoya or in NobleProg corporate training centers in Nagoya.
Nagoya is the third largest city in Japan. This open office is located in the district surrounding Nagoya station. Since 2015...
Nagoya is the third largest city in Japan. This open office is located in the district surrounding Nagoya station. Since 2015, landmark buildings kept inaugurating around the station and by 2017, it is predictable that many corporations will choose to be in this Meieki area. Furthermore, with the magnetic-levitated linear shinkansen to start service in 2027, Nagoya has strategic advantage.
Nagoya and Aichi area is known for its auto industry like Toyota. Besides automobiles, there are many head offices of Japanese manufacturers. With their affiliated companies and partners located in the same area, the city itself is a business complex. In recent years, forward-thinking IT companies are also trying to get a foothold in Nagoya. Besides all the attention-gathering news on the redevelopment, Meieki area has good access to Tokyo, Osaka and other major cities. It is also convenient for sales activity and commute. Therefore, it is a popular yet practical business district.
This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.
MLlib is Spark’s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.
It divides into two packages:
spark.mllib contains the original API built on top of RDDs.
spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.
Audience
This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark
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Testimonials (2)
A lot of practical examples, different ways to approach the same problem, and sometimes not so obvious tricks how to improve the current solution
Rafał - Nordea
Course - Apache Spark MLlib
This is one of the best hands-on with exercises programming courses I have ever taken.
Laura Kahn
Course - Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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