Newsletter

Holen Sie sich die neuesten Updates von Hortonworks per E-Mail

Einmal monatlich erhalten Sie die neuesten Erkenntnisse, Trends und Analysen sowie Fachwissen zu Big Data.

AVAILABLE NEWSLETTERS:

Sign up for the Developers Newsletter

Einmal monatlich erhalten Sie die neuesten Erkenntnisse, Trends und Analysen sowie Fachwissen zu Big Data.

cta

Erste Schritte

Cloud

Sind Sie bereit?

Sandbox herunterladen

Wie können wir Ihnen helfen?

* Ich habe verstanden, dass ich mich jederzeit abmelden kann. Außerdem akzeptiere ich die weiteren Angaben in der Datenschutzrichtlinie von Hortonworks.
SchließenSchaltfläche „Schließen“
cta
HDP Data Science

Überblick

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.

Voraussetzungen

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

Live-Training

Live-Training Im eigenen Tempo Kombiniert
LIVE-KURS
DATE & TIME
LOCATION
REGISTRIEREN