Apache Spark MLlib Training Course

Primary tabs

Duration Duration

35 hours (usually 5 days including breaks)

Requirements Requirements

Knowledge of one of the following:

  • Java
  • Scala
  • Python
  • SparkR.

Overview Overview

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

Course Outline Course Outline

spark.mllib: data types, algorithms, and utilities

  • Data types
  • Basic statistics
    • summary statistics
    • correlations
    • stratified sampling
    • hypothesis testing
    • streaming significance testing
    • random data generation
  • Classification and regression
    • linear models (SVMs, logistic regression, linear regression)
    • naive Bayes
    • decision trees
    • ensembles of trees (Random Forests and Gradient-Boosted Trees)
    • isotonic regression
  • Collaborative filtering
    • alternating least squares (ALS)
  • Clustering
    • k-means
    • Gaussian mixture
    • power iteration clustering (PIC)
    • latent Dirichlet allocation (LDA)
    • bisecting k-means
    • streaming k-means
  • Dimensionality reduction
    • singular value decomposition (SVD)
    • principal component analysis (PCA)
  • Feature extraction and transformation
  • Frequent pattern mining
    • FP-growth
    • association rules
    • PrefixSpan
  • Evaluation metrics
  • PMML model export
  • Optimization (developer)
    • stochastic gradient descent
    • limited-memory BFGS (L-BFGS)

spark.ml: high-level APIs for ML pipelines

  • Overview: estimators, transformers and pipelines
  • Extracting, transforming and selecting features
  • Classification and regression
  • Clustering
  • Advanced topics

Bookings, Prices and Enquiries

Guaranteed to run even with a single delegate!
Public Classroom Public Classroom
From $12900
Request
Public Classroom
Participants from multiple organisations. Topics usually cannot be customised
Private Classroom
Participants are from one organisation only. No external participants are allowed. Usually customised to a specific group, course topics are agreed between the client and the trainer.
Private Remote
The instructor and the participants are in two different physical locations and communicate via the Internet

The more delegates, the greater the savings per delegate. Table reflects price per delegate and is used for illustration purposes only, actual prices may differ.

Number of Delegates Public Classroom Private Remote
1 $12900 $7350
2 $7200 $4375
3 $5300 $3383
4 $4350 $2888
Cannot find a suitable date? Choose Your Course Date >>
Too expensive? Suggest your price

Related Categories

Related Courses


Course Discounts

Course Discounts Newsletter

We respect the privacy of your email address. We will not pass on or sell your address to others.
You can always change your preferences or unsubscribe completely.

Some of our clients