Apache Spark MLlib Training Courses

Apache Spark MLlib Training

MLlib is Apache Spark's scalable machine learning library.

Apache Spark MLlib Course Outlines

Code Name Duration Overview
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
aitech Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP 21 hours Distribution big data Data mining methods (training single systems + distributed prediction: traditional machine learning algorithms + Mapreduce distributed prediction) Apache Spark MLlib Recommendations and Advertising: Natural language Text clustering, text categorization (labeling), synonyms User profile restore, labeling system Recommended algorithms Insuring the accuracy of "lift" between and within categories How to create closed loops for recommendation algorithms Logical regression, RankingSVM, Feature recognition (deep learning and automatic feature recognition for graphics) Natural language Chinese word segmentation Theme model (text clustering) Text classification Extract keywords Semantic analysis, semantic parser, word2vec (vector to word) RNN long-term memory (TSTM) architecture

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