Apache Spark MLlib Training in Reading

MLlib is Apache Spark's scalable machine learning library.
Reading TVP
Client Testimonials
Apache Spark MLlib Course Events - Reading
Code | Name | Venue | Duration | Course Date | PHP | Course Price [Remote / Classroom] |
---|---|---|---|---|---|---|
aitech | Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP | Reading TVP | 21 hours | Wed, 2018-05-09 09:30 | £3900 / £4695 | |
spmllib | Apache Spark MLlib | Reading TVP | 35 hours | Mon, 2018-06-25 09:30 | £6500 / £7825 | |
aitech | Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP | Reading TVP | 21 hours | Mon, 2018-07-02 09:30 | £3900 / £4695 | |
spmllib | Apache Spark MLlib | Reading TVP | 35 hours | Mon, 2018-08-20 09:30 | £6500 / £7825 | |
aitech | Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP | Reading TVP | 21 hours | Wed, 2018-08-29 09:30 | £3900 / £4695 | |
spmllib | Apache Spark MLlib | Reading TVP | 35 hours | Mon, 2018-10-15 09:30 | £6500 / £7825 | |
aitech | Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP | Reading TVP | 21 hours | Mon, 2018-10-22 09:30 | £3900 / £4695 |
Course Outlines
Code | Name | Duration | Outline |
---|---|---|---|
spmllib | Apache Spark MLlib | 35 hours |
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:
Audience This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark |
aitech | Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP | 21 hours |
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. |