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.
What Is Machine Learning?
Получаемые навыки
Data Analysis, Python Programming, Machine Learning, Exploratory Data Analysis
Рецензии
KP
May 07, 2020
Clear and explanatory approach to the object. Instructors have great teaching transmissibility.
EM
Jun 26, 2016
Good introduction with python example for famous algorithm such as random forest and k-mean
In this session, you will learn about decision trees, a type of data mining algorithm that can select from among a large number of variables those and their interactions that are most important in predicting the target or response variable to be explained. Decision trees create segmentations or subgroups in the data, by applying a series of simple rules or criteria over and over again, which choose variable constellations that best predict the target variable.
Преподаватели
Jen Rose
Research Professor
Lisa Dierker
Professor
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