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Analytics And Big Data

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last update : 02/01/2017

Machine Learning with Big Data

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The art and science of making sense of data is a highly sought after skill in today’s data driven world.Data science isn’t just for data scientists. In massively connected data driven world, it is imperative that the workforce of today and tomorrow is able to understand what data is available and use scientific methods to analyze and interpret it. Data science is now recognized as a highly-critical growth area with impact across many sectors including science, government, finance, health care, telecom, manufacturing, advertising, retail, and others. Launch your data science career with this practical workshop. Build a solid foundation in machine learning using R and start exploring data-related careers.

TOP 9 REASONS TO ATTEND

  • Understand the art and science of discovering patterns and making intelligent predictions from big data.
  • Define machine learning, why it matters, and discuss its relationship to analytics, data science, and big data.
  • Machine learning fundamentals, the importance of algorithms, and machine learning as a service.
  • Basics of R platform, programming language concepts, common and useful R commands, and applying machine learning methods.
  • Doing machine learning - Understanding the steps in the machine learning pipeline, from data acquisition and feature generation, to training and model selection.
  • Practically learn the most commonly used machine learning methods, covering both supervised and unsupervised learning.
  • Develop understanding of which algorithm to choose based on the analytics challenge and the data you have.
  • Be able to appreciate the trade-offs involved in choosing particular techniques for particular problems.
  • Discover how to understand, interpret and convey the results of data science life cycle.

AGENDA

The workshop has a strong focus on gaining hands-on experience implementing algorithms and building predictive models on real datasets. By the end of the 3 days, participants will be ready to implement the machine learning algorithms using data science on your own data, and immediately generate value. The workshop will take participants through the conceptual and applied foundations of the subject. Topics covered include:

  • R for Statistical Analysis and Machine Learning
  • Machine learning theory, types of learning
  • Techniques, models and methods

Labs are developed to practically learn how to use the R programming language and packages for applying the main concepts and techniques of data science and machine learning.

Day 1:

Data Science and Machine Learning – Context, Toolkit and Industry Practices

  • A data-driven digital world, introduction to Data Science and component parts of Data Science
  • Enterprise Big Data platform architectures, Hadoop ecosystem and Apache Spark
  • Data Science Toolkit and Life Cycle – A strategy to approach data analytics problems
  • Fundamentals of Machine Learning for Data Science
  • R for Statistical Analysis and Machine Learning
  • Basics of R Programming

Day 2:

R Programming and Applications of Machine Leaning Models

  • Pre-processing and Machine Learning Workflow   
  • Guest Speaker slot
  • Model Selection: Training, Validating and Testing
  • Regression Models  

Day 3:

Advanced Predictive Methods and Capstone Project

  • Classification Models  
  • Ensemble Methods  
  • Capstone Project  

 

 

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The art and science of making sense of data is a highly sought after skill in today’s data driven world.Data science isn’t just for data scientists. In massively connected data driven world, it is imperative that the workforce of today and tomorrow is able to understand what data is available and use scientific methods to analyze and interpret it. Data science is now recognized as a highly-critical growth area with impact across many sectors including science, government, finance, health care, telecom, manufacturing, advertising, retail, and others. Launch your data science career with this practical workshop. Build a solid foundation in machine learning using R and start exploring data-related careers.

TOP 9 REASONS TO ATTEND

  • Understand the art and science of discovering patterns and making intelligent predictions from big data.
  • Define machine learning, why it matters, and discuss its relationship to analytics, data science, and big data.
  • Machine learning fundamentals, the importance of algorithms, and machine learning as a service.
  • Basics of R platform, programming language concepts, common and useful R commands, and applying machine learning methods.
  • Doing machine learning - Understanding the steps in the machine learning pipeline, from data acquisition and feature generation, to training and model selection.
  • Practically learn the most commonly used machine learning methods, covering both supervised and unsupervised learning.
  • Develop understanding of which algorithm to choose based on the analytics challenge and the data you have.
  • Be able to appreciate the trade-offs involved in choosing particular techniques for particular problems.
  • Discover how to understand, interpret and convey the results of data science life cycle.

 

 

QUICK QUERY

 
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