Machine Learning Foundations - A Case Study Approach Coursera

Overview

Do you have data and wonder what it can tell you?  Do you need a deeper understanding of the core ways in which machine learning can improve your business?  Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?

In this Machine Learning Foundations - A Case Study Approach course offered by Coursera in partnership with University of Washington, you will get hands-on experience with machine learning from a series of practical case-studies. 

Learning Outcomes:  By the end of this course, you will be able to:

• Describe the core differences in analyses enabled by regression, classification, and clustering.

• Select the appropriate machine learning task for a potential application.  

• Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.

• Represent your data as features to serve as input to machine learning models. 

• Assess the model quality in terms of relevant error metrics for each task.

• Utilize a dataset to fit a model to analyze new data.

• Build an end-to-end application that uses machine learning at its core.  

• Implement these techniques in Python.

Skills you'll gain

• Algorithms

• Applied Machine Learning

• Human Learning

• Machine Learning

• Machine Learning Algorithms

• Data Analysis

• Python Programming

• Problem Solving

• Computer Programming

• Regression