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SAS

Statistics You Need to Know for Machine Learning

SAS via Coursera

Overview

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When it comes to using data, there are two main camps, traditional statistics and machine learning, and the two camps complement each other. Statistics remains highly relevant, irrespective of the size of data. Its role remains what it has always been, but it is even more important now. There is a need to transition from traditional statistical modeling to the machine learning world. This course introduces the statistical background necessary for machine learning. Knowledge of statistics relevant to machine learning will prepare you to become a data scientist. The course prepares you for future instruction on machine learning (including its underlying methodology that has statistical foundations) and enables you to develop a deeper understanding of machine learning models. This course is aimed at anyone in the field of data science who does not yet have a deep understanding of statistical and machine learning concepts or wants to enhance their knowledge, which might include business analysts, data analysts, marketing analysts, marketing managers, data scientists, data engineers, financial analysts, data miners, statisticians, mathematicians, and others who work in allied areas.

Syllabus

  • Statistics and Machine Learning
    • In this module, you learn how to explain the relevance of statistics in big data and machine learning and relate statistical and data science terminology.
  • Fundamental Statistical Concepts
    • In this module, you learn about fundamental statistical concepts.
  • Explanatory Modeling Using Linear Regression
    • In this module, you learn about correlation and simple linear regression, multiple regression and model selection, and model diagnostics.
  • Predictive Modeling Using Logistic Regression
    • In this module, you learn about predictive modeling using logistic regression.
  • Statistical Foundations of Machine Learning
    • In this module, you learn about the statistical foundations of machine learning.

Taught by

Catherine Truxillo

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