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.
Über diesen Kurs
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.
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Top-Bewertungen von MACHINE LEARNING FOR DATA ANALYSIS
Since it is a part of a specialization, the topics start somewhere in between and is only recommended for those who have completed the previous courses with in these specialization.
More examples in coding and results are expected. So it is more convenient for students to compare different results and understand deeper
More Implementation oriented and less math also contains distracting background videos when explaining important concepts
Clear and explanatory approach to the object. Instructors have great teaching transmissibility.
Über den Spezialisierung 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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