Apache Spark MLlib Treningskurs

Kurskode

spmllib

Varighet

35 timer (vanligvis 5 dag inkludert pauser)

Krav

Knowledge of one of the following:

  • Java
  • Scala
  • Python
  • SparkR.

Oversikt

MLlib er Sparks maskinlæringsbibliotek. Målet er å gjøre praktisk maskinlæring skalerbar og enkel. Den består av vanlige læringsalgoritmer og verktøy, inkludert klassifisering, regresjon, klynger, samarbeidende filtrering, dimensjonalitetsreduksjon, samt primitiver på lavere nivå og optimaliseringsgrensesnitt på rørledningen.

Den deler seg i to pakker:

  • spark.mllib inneholder den originale API-en som er bygget på toppen av RDD-er.

  • spark.ml gir API på høyere nivå bygget oppå DataFrames for konstruksjon av ML-rørledninger.

Publikum

Dette kurset er rettet mot ingeniører og utviklere som søker å bruke et innebygd maskinbibliotek for Apache Spark

Machine Translated

Kursplan

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

Testimonials

★★★★★
★★★★★

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