Apache Spark MLlib培训

Apache Spark MLlib培训

MLlib is Apache Spark's scalable machine learning library.

Apache Spark MLlib大纲

代码 名字 期限 概览
spmllib Apache Spark MLlib 35小时 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
aitech Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP 21小时 大数据下的分布式  数据挖掘方法(训练单机型+分布式的预测: 传统机器学习算法+Mapreduce 分布式预测,) Apache Spark MLlib 推荐与广告精准投放: 自然语言的部分 文本聚类,文本分类(标签),同义词 用户profile还原,标签体系 推荐算法的策略 类之间的lift, 类内的lift, 如何精准 如何构建推荐算法的闭环 逻辑回归,RankingSVM, 特征识别:(深度学习与图形的自动特征识别) 自然语言 中文分词 主题模型(文本聚类) 文本分类 提取关键词 语义分析 sementic parser, word2vec到词向量 RNN Long short-term memory (TSTM) Architecture
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