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Machine Learning for Data Analysis

Wesleyan University via Coursera

Overview

Are you interested in predicting future outcomes using your data? This course helps you do just that! Machine learning is the process of developing, testing, and applying predictive algorithms to achieve this goal. Make sure to familiarize yourself with course 3 of this specialization before diving into these machine learning concepts. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering. By completing this course, you will learn how to apply, test, and interpret machine learning algorithms as alternative methods for addressing your research questions.

Taught by

Jen Rose

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Reviews

3.5 rating, based on 4 reviews

Start your review of Machine Learning for Data Analysis

  • Caio Taniguchi is taking this course right now, spending 2 hours a week on it and found the course difficulty to be very easy.

    Very shallow course. I had just finished Andrew Ng's Machine Learning class and was looking for something to continue to build my knowledge in ML when I bumped into this course. It looked very interesting, as it covered algorithms that were not explored...
  • Anonymous

    Anonymous completed this course.

    I thought the course was a fantastic way to introduce these concepts, which are becoming more widely used for data analysis
  • Colin Khein completed this course.

  • Alvaro Carrera is taking this course right now.

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