Machine Learning Foundations

A Case Study Approach

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 course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.

This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications.

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

-Identify potential applications of machine learning in practice.

-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.

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From Coursera
Institution University of Washington
Instructors Carlos Guestrin, Emily Fox
Length 6 weeks of study, 5-8 hours/week
Price Free (with limitations) or $79 for a Verified Certificate
Language English (English)
Subjects Data Science Machine Learning
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What learners are saying BETA

5 stars if

Would be 5 stars if all tools are open source.Great Course…

Would have rated 5 stars if an open source library like scikit-learn were used… complete this course but so far I have completed, the course is excellent…

open source tools

...if you are really interested you could do them with open source tools.It is amazing…

...a trial version of a pretty expensive library.I love the case…

am looking forward

...not as detailed and thorough as I expected.

case study approach

I give Machine Learning Foundations: A Case Study Approach 4.5 out of 5 stars…

Looking forward to the next courses in the specialization!Amazing for beginners…

Loved the case study approach !Well structured and engaging…

Fortement conseillé aux novices.The case study approach for explaining machine learning concepts is commendable…

real world examples

...helps in making decisions when it comes to real-life applications…

Looking forward to see other courses.A very nice introduction to fundamentals of Machine Learning…

this course should add more document and instructions on how to use Graphlab.Easy and simple…

This course teaches valuable skills*


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