ML & Spark: MLlib intro and exercises, in the limit of
15 places – No more seats left (if you apply we will keep you in waiting mode for next sessions or in case a new place becomes available for this session)
TRAINER: ALEX SISU
- Data pre-processing
- Clustering algorithms
- Classification algorithms: RandomForest, GradientBoostedTrees
- Topic analysis (LDA)
Examples: document classification, user preferences on shopping items, price forecasting.
- knowledge of java or scala programming is a must.
- knowledge of scala/java collections is a must
- basic programming notions
- knowledge of spark basics (RDD, map/reduce operators) is a must
- basic knowledge of maven is a must
- function Eclipse/Intellij IDE with maven plugin, necessary for importing a maven project
- basic mathematical skills
Register here for next session: