newsletter

Obtenha atualizações recentes da Hortonworks por e-mail

Uma vez por mês, receba os mais recentes insights, tendências, informações analíticas e conhecimentos sobre Big Data.

AVAILABLE NEWSLETTERS:

Sign up for the Developers Newsletter

Uma vez por mês, receba os mais recentes insights, tendências, informações analíticas e conhecimentos sobre Big Data.

cta

Comece a Usar

nuvem

Pronto para começar?

Baixar sandbox

Como podemos ajudá-lo?

* Eu entendo que posso cancelar a inscrição a qualquer momento. Eu também reconheço as informações adicionais encontradas na Política de Privacidade da Hortonworks.
fecharBotão Fechar
cta
Ciência de Dados HDP

Visão Geral

This course provides instruction on the theory and practice of data science, including machine learning and natural language processing. This course introduces many of the core concepts behind today’s most commonly used algorithms and introducing them in practical applications. We’ll discuss concepts and key algorithms in all of the major areas – Classification, Regression, Clustering, Dimensionality Reduction, including a primer on Neural Networks. We’ll focus on both single-server tools and frameworks (Python, NumPy, pandas, SciPy, Scikit-learn, NLTK, TensorFlow Jupyter) as well as large-scale tools and frameworks (Spark MLlib, Stanford CoreNLP, TensorFlowOnSpark/Horovod/MLeap, Apache Zeppelin). Download the data sheet to view the full list of objectives and labs.

Prerequisites

Students must have experience with Python and Scala, Spark, and prior exposure to statistics, probability, and a basic understanding of big data and Hadoop principles. While brief reviews are offered in these topics, students new to Hadoop are encouraged to attend the Apache Hadoop Essentials (HDP-123) course and HDP Spark Developer (DEV-343), as well as the language-specific introduction courses.


Target Audience


Architects, software developers, analysts and data scientists who need to apply data science and machine learning on Spark/Hadoop
.

1
Day

An Introduction to Data Science, SciKit-Learn, HDFS, Reviewing Spark apps, DataFrames and NOSQL

Objectives

  • Discuss aspects of Data Science, the team members, and the team roles
  • Discuss use cases for Data Science
  • Discuss the current State of the Art and its future direction
  • Review HDFS, Spark, Jupyter, and Zeppelin
  • Work with SciKit-Learn, Pandas, NumPy, Matplotlib, and Seaborn

Labs

  • Hello, ML w/ SciKit-Learn
  • Spark REPLs, Spark Submit, & Zeppelin Review
  • HDFS Review
  • Spark DataFrames and Files
  • NiFi Review

Algorithms in Spark ML and SciKit-Learn: Linear Regression, Logistic Regression, Support Vectors, Decision Trees

K-Means & GMM Clustering, Essential TensorFlow, NLP with NLTK, NLP with Stanford CoreNLP

HyperParameter Tuning, K-Fold Validation, Ensemble Methods, ML Pipelines in SparkML

Treinamento ao vivo

Treinamento ao vivo Individualizada COMBINADO
AULA AO VIVO
DATE & TIME
LOCATION
REGISTRE-SE