Apache Spark MLlib Training Courses
MLlib is Apache Spark's scalable machine learning library.
Apache Spark MLlib Course Outlines
|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: 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 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|
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