endymecy/spark-ml-source-analysis resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1957 stars · 815 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A Chinese-language tutorial collection analyzing the principles and source code of Spark MLlib's machine learning algorithms. It covers classification, regression, clustering, dimensionality reduction, feature engineering, and optimization with their distributed implementations.

Use cases

• understand how spark mllib algorithms are implemented internally
• learn distributed implementations of machine learning algorithms
• study spark ml source code for classification and clustering algorithms
• learn how k-means or ALS work in spark
• prepare for spark machine learning interviews
• understand gradient descent and L-BFGS implementations in spark

When to choose

• you work with Spark 1.6/2.x MLlib and want deep algorithm and source-level understanding
• you prefer Chinese-language explanations of ML theory plus code walkthroughs
• you want to learn how ML algorithms are distributed across a cluster

When to avoid

• you need up-to-date coverage of Spark 3.x or the newer spark.ml DataFrame API
• you want runnable production code rather than analysis articles
• you need English-language documentation

Facets

learning-resource · maturity maintenance

machine-learning documentation machine-learning big-data tutorials jvm python spark spark-ml source-code-analysis distributed-machine-learning chinese-language

Member repositories

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markdown · JSON · MCP: product_card(name="endymecy/spark-ml-source-analysis")