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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
Requirements
Knowledge of one of the following:
- Java
- Scala
- Python
- SparkR.
35 Hours
Testimonials (1)
A lot of practical examples, different ways to approach the same problem, and sometimes not so obvious tricks how to improve the current solution
Rafał - Nordea
Course - Apache Spark MLlib
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