Apache Spark MLlib Training in Plymouth

Apache Spark MLlib Training in Plymouth

MLlib is Apache Spark's scalable machine learning library.

Plymouth Drake Circus

Regus Drake Circus
Unit MSU9A, Level 1 1 Charles Street
Plymouth PL1 1EA
United Kingdom
GB
Plymouth Drake Circus
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Apache Spark MLlib Course Events - Plymouth

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
spmllib Apache Spark MLlib Plymouth Drake Circus 35 hours Mon, 2018-02-26 09:30 £6500 / £7250
aitech Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP Plymouth Drake Circus 21 hours Tue, 2018-03-06 09:30 £3900 / £4350
spmllib Apache Spark MLlib Plymouth Drake Circus 35 hours Mon, 2018-04-23 09:30 £6500 / £7250
aitech Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP Plymouth Drake Circus 21 hours Wed, 2018-05-02 09:30 £3900 / £4350
spmllib Apache Spark MLlib Plymouth Drake Circus 35 hours Mon, 2018-06-25 09:30 £6500 / £7250
aitech Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP Plymouth Drake Circus 21 hours Tue, 2018-06-26 09:30 £3900 / £4350

Course Outlines

Code Name Duration Outline
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

This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.

  1. Distribution big data
    1. Data mining methods (training single systems + distributed prediction: traditional machine learning algorithms + Mapreduce distributed prediction)
    2. Apache Spark MLlib
  2. Recommendations and Advertising:
    1. Natural language
    2. Text clustering, text categorization (labeling), synonyms
    3. User profile restore, labeling system
    4. Recommended algorithms
    5. Insuring the accuracy of "lift" between and within categories
    6. How to create closed loops for recommendation algorithms
  3. Logical regression, RankingSVM,
  4. Feature recognition (deep learning and automatic feature recognition for graphics)
  5. Natural language
    1. Chinese word segmentation
    2. Theme model (text clustering)
    3. Text classification
    4. Extract keywords
    5. Semantic analysis, semantic parser, word2vec (vector to word)
    6. RNN long-term memory (TSTM) architecture

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