Apache Spark MLlib 교육 과정

  1. 빅 데이터 Training
  2. Apache Spark Training
  3. Apache Spark MLlib Training
  4. Apache Spark MLlib 교육 과정

Course Code

spmllib

Duration

35 hours (usually 5 days including breaks)

Requirements

Knowledge of one of the following:

  • Java
  • Scala
  • Python
  • SparkR.

Overview

MLlib는 Spark의 기계 학습 (ML) 라이브러리입니다. 그 목표는 실용적인 기계 학습을 확장 가능하고 쉽게 만들어주는 것입니다. 분류, 회귀, 클러스터링, 협업 필터링, 차원 감소, 하위 레벨 최적화 프리미티브 및 상위 레벨 파이프 라인 API 등 일반적인 학습 알고리즘과 유틸리티로 구성됩니다.

두 개의 패키지로 나뉩니다.

  • spark.mllib는 RDD 위에 구축 된 원래 API를 포함합니다.

  • spark.ml은 ML 파이프 라인을 구성하기 위해 DataFrames 위에 구축 된 고급 API를 제공합니다.

청중

이 과정은 Apache Spark 용 내장 라이브러리를 활용하고자하는 엔지니어 및 개발자를 대상으로합니다.

Machine Translated

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

회원 평가

★★★★★
★★★★★

Related Categories

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