Ementorhub

What you'll learn

Express why Statistical Learning is important and how it can be used.

Identify the strengths, weaknesses and caveats of different models and choose the most appropriate model for a given statistical problem.

Determine what type of data and problems require supervised vs. unsupervised techniques.

Skills you'll gain

Statistical Inference

Machine Learning Algorithms

Statistical Analysis

Applied Machine Learning

Statistical Modeling

Supervised Learning

Data Science

Classification And Regression Tree (CART)

Statistical Methods

Regression Analysis

Unsupervised Learning

Predictive Modeling

Statistical Machine Learning

There are 6 modules in this course

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

Accuracy

Simple Linear Regression

Multiple Linear Regression

Classification Overview

Classification Models