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PRODID:-//Analytics Center - ECPv4.6.16//NONSGML v1.0//EN
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X-WR-CALNAME:Analytics Center
X-ORIGINAL-URL:http://analyticscenter.com
X-WR-CALDESC:Events for Analytics Center
BEGIN:VEVENT
DTSTART;VALUE=DATE:20190328
DTEND;VALUE=DATE:20190330
DTSTAMP:20190325T074808
CREATED:20180527T153546Z
LAST-MODIFIED:20190311T105118Z
UID:642-1553731200-1553903999@analyticscenter.com
SUMMARY:Machine Learning on Big Data
DESCRIPTION:From data munging to evaluating models\, “Machine Learning on Big Data” is a 2-days course covering the entire Data Science pipeline: converting collected Big Data into mathematical data structures\, algorithms for learning distributed regression\, classification and recommender system models\, implementing such models using Apache Mahout and Apache Spark\, assessing how the models work. In this course several data mining/machine learning models and scalable learning algorithms are covered\, including: \n\nGeneralized Linear Models: Linear Regression and Logistic Regression\nDecision Trees\nClustering\nMixed Membership Models: (Latent Dirichlet Allocation)\nSimilarity Analysis\nMatrix Factorization\nLearning Ensembles: Random Forests\n\nThe participants will get familiar with the cutting-edge open source Machine Learning libraries\, and run Machine Learning pipelines on pre-installed Hadoop/Spark clusters provided by Analytics Center. \nHands-on Labs \nWith hands-on labs and demonstrations\, the participants will utilize ecosystem tools to: \n\nPrepare a prototyping (interactive notebook) environment for large scale data analysis\nTransform Big Data into Machine Learning data structures\nUnderstand scalable Machine Learning algorithms\nWrite programs to learn and evaluate supervised learning models\nWrite programs for clustering data\nWrite programs for finding mixed memberships to clusters (topic modeling)\nWrite programs to learn and evaluate recommender system models\nDesign a typical analytics/machine learning pipeline\n\nCourse Prerequisites \n“Machine Learning on Big Data” is the core course for the Data Scientist learning track. In addition to an appreciation of what Machine Learning is capable of\, the attendees are expected to have an understanding of how Big Data Processing technologies work in general. \nThe attendees should be able to write simple programs either in Scala or Python\, but the amount of programming is minimal. \nCourse Coverage \nPART I – ESSENTIALS & ECOSYSTEM \nMachine Learning Essentials & Big Learning \n\nLearning from Data\nCommon Machine Learning Tasks\nExample Use Cases\nMachine Learning in the Big Data Era\nMachine Learning on Big Data: Challenges\nMachine Learning Pipelines:\n\nData Preparation/Transformation\nLearning\nEvaluation\nDeployment\n\n\n\n \nBig Data Science Ecosystem \n\nApache Spark\n\nApache Spark Basics\nRDD APIs: Scala API and PySpark\nSpark DataFrames (and Datasets)\nSpark ML Pipelines\n\n\nApache Zeppelin for Interactive Data Analysis Notebooks\n\n \nPART II – ALGORITHMS & INTERNALS \nData Munging \n\nSummarizing Large Datasets\nCommon Data Transformation Tasks\nData Structures for Machine Learning\nWorking with Text Data\n\n \nSupervised Learning \n\nLearning & Evaluation using Spark APIs:\n\nLinear Regression\nLogistic Regression\nNaive Bayes\nDecision & Regression Trees\nTree Ensembles: Random Forests\n\n\nEvaluation\nMaking Predictions\n\n \nUnsupervised Learning \n\nClustering:\n\nK-Means\nGaussian Mixture\n\n\nMixed Membership: Latent Dirichlet Allocation\nOnline Clustering from Data Streams: Streaming K-Means\n\n \nRecommender Systems \n\nSimilarity Based Collaborative Filtering\nMatrix Factorization Based Collaborative Filtering\nEvaluating Recommender Systems\n\n \nLarge Scale Machine Learning Internals \n\nDistributed Optimization for Large Scale Supervised Learning\nK-Means/Streaming K-Means at Large Scale\nVariational EM for Learning & Inference in LDA\nAlternating Least Squares (ALS) and Implicit ALS for Matrix Factorization based Collaborative Filtering\n\n
URL:http://analyticscenter.com/trainings/machine-learning-on-big-data/
LOCATION:Adres\, Istanbul\, 34345\, Turkey
GEO:41.0631446;29.0358096
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CATEGORIES:Machine Learning on Big Data
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