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.
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Habilidades que obtendrás
Wesleyan University, founded in 1831, is a diverse, energetic liberal arts community where critical thinking and practical idealism go hand in hand. With our distinctive scholar-teacher culture, creative programming, and commitment to interdisciplinary learning, Wesleyan challenges students to explore new ideas and change the world. Our graduates go on to lead and innovate in a wide variety of industries, including government, business, entertainment, and science.
- 5 stars56,86 %
- 4 stars25,55 %
- 3 stars7,98 %
- 2 stars4,15 %
- 1 star5,43 %
Principales reseñas sobre MACHINE LEARNING FOR DATA ANALYSIS
Good introduction with python example for famous algorithm such as random forest and k-mean
Very good course. I recommend to anyone who's interested in data analysis and machine learning.
Clear and explanatory approach to the object. Instructors have great teaching transmissibility.
I enjoyed this course a lot. It's easy and I've learnt what I need to apply the machine learning techniques. Easy and simple. You don't need to be a mathematician.
Acerca de Programa especializado: Data Analysis and Interpretation
Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions.
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