ODKAZ: https://www.skoleni-kurzy.eu/kurz-75070



Kurz: Data Science Training


DataScript s.r.o.


This four-day workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models t ...


Cena kurzu:
     ...   2.180 EUR / Kurz  

     ... včetně DPH: 2.638 EUR / Kurz

Objednat - pro přihlášení na kurz/školení klikněte na zvolený termín školení a místo konání



Přihláška na školení:

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  • -- ... pro objednání kurzu klikněte na zvolený termín školení a/nebo je možno poslat:



    Popis kurzu
    Data Science Training


    Kurz je určen pro ...

    Audience

    • This course is suitable for developers, data analysts, and statisticians with basic knowledge of Apache Hadoop: HDFS, MapReduce, Hadoop Streaming, and Apache Hive as well as experience working in Linux environments.


    Lektoři kurzu

    Lektoři z firmy: DataScript s.r.o.


    [Kurz] Program kurzu (obsah přednášky/semináře/rekvalifikace/studia) ...

    Goals The workshop includes brief lectures, interactive demonstrations, hands-on exercises, and discussions covering topics including:

    • Overview of data science and machine learning at scale
    • Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue
    • Introduction to Cloudera Data Science Workbench
    • Overview of Apache Spark 2
    • Reading and writing data
    • Inspecting data quality
    • Cleansing and transforming data
    • Summarizing and grouping data
    • Combining, splitting, and reshaping data
    • Exploring data
    • Configuring, monitoring, and troubleshooting
    • Spark applications
    • Overview of machine learning in
    • Spark MLlib Extracting, transforming, and selecting features
    • Building and evaluating regression models
    • Building and evaluating classification models
    • Building and evaluating clustering models
    • Cross-validating models and tuning hyperparameters
    • Building machine learning pi
      * The workshop includes brief lectures, interactive demonstrations, hands-on exercises, and discussions covering topics including:
      • Overview of data science and machine learning at scale
      • Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue
      • Introduction to Cloudera Data Science Workbench
      • Overview of Apache Spark 2
      • Reading and writing data
      • Inspecting data quality
      • Cleansing and transforming data
      • Summarizing and grouping data
      • Combining, splitting, and reshaping data
      • Exploring data
      • Configuring, monitoring, and troubleshooting
      • Spark applications
      • Overview of machine learning in
      • Spark MLlib Extracting, transforming, and selecting features
      • Building and evaluating regression models
      • Building and evaluating classification models
      • Building and evaluating clustering models
      • Cross-validating models and tuning hyperparameters
      • Building machine learning pipelines
      • Deploying machine learning models
      Outline Read the entire course outline for more details. Prerequisites
      • Workshop participants should have a basic understanding of Python or R and some experience exploring and analyzing data and developing statistical or machine learning models. Knowledge of Hadoop or Spark is not required.



    [Kurz] Cíl školení / poznámka ke kurzu...

    Goals The workshop includes brief lectures, interactive demonstrations, hands-on exercises, and discussions covering topics including:
    • Overview of data science and machine learning at scale
    • Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue
    • Introduction to Cloudera Data Science Workbench
    • Overview of Apache Spark 2
    • Reading and writing data
    • Inspecting data quality
    • Cleansing and transforming data
    • Summarizing and grouping data
    • Combining, splitting, and reshaping data
    • Exploring data
    • Configuring, monitoring, and troubleshooting
    • Spark applications
    • Overview of machine learning in
    • Spark MLlib Extracting, transforming, and selecting features
    • Building and evaluating regression models
    • Building and evaluating classification models
    • Building and evaluating clustering models
    • Cross-validating models and tuning hyperparameters
    • Building machine learning pipelines
    • Deploying machine learning models

    [Školení] Další popis kurzu (úroveň, minimální znalosti, informace o cenách kurzu) ...

    Prerequisites

    • Workshop participants should have a basic understanding of Python or R and some experience exploring and analyzing data and developing statistical or machine learning models. Knowledge of Hadoop or Spark is not required.




    ODKAZ: https://www.skoleni-kurzy.eu/kurz-75070


    Podobné kurzy #Data Science Training >>






    Poslední aktualizace: 2024-05-02 00:42:23

    DataScript s.r.o.
    skoleni-kurzy.eu