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Machine Learning Foundations: A Case Study Approach, University of Washington

4.6
Оценки: 7,886
Рецензии: 1,934

Об этом курсе

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....

Лучшие рецензии

автор: BL

Oct 17, 2016

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much

автор: DS

Sep 28, 2015

Excellent course, with really good lectures, material and assignment. Plus the professors are really amazing and their enthusiasm is really refreshing and makes the class more interesting. Loved it!

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Рецензии: 1,858

автор: SaketKr

Dec 09, 2018

It was really good.

Pros:

Has really nice assignments.

Teaching is really good.

Cons:

Should've used and open source package. Graphlab is good, I accept, but I wasted like 4-5 hours trying to install it, because some or other errors or dependencies,. I mean some consideration should've been done about an easy smooth method for it, for a beginner like me, it was really frustrating.

автор: Christopher Manhave

Dec 07, 2018

This was a great course. The instructors were fun and knowledgeable and the assignments were well-written. I loved the flexibility of being allowed to use whatever software I wanted to solve the ML assignments since the quizzes were based on the results of the modeling rather than submitted code. For some assignments I used sklearn and for others I used the software recommended by the instructors (graphlab).

автор: Muhammad Amin Nadim

Dec 06, 2018

Excellent

автор: Zohaib Mushtaq

Dec 05, 2018

very good and excellent course.

автор: Shalini Garg

Dec 04, 2018

Nice course to understand the basics of Machine Learning

автор: Luis Guillermo Andino Pinto

Dec 04, 2018

Good introductory course, some videos require more than one time to review them and even doing external research on the topics to understand well the concepts

автор: Hakim Laukkoski

Dec 03, 2018

Good course despite the technical issues with GraphLab Create in Coursera Notebook.

автор: Xue

Dec 02, 2018

Great course!

автор: Praveen kumar

Dec 01, 2018

Good case studies to start with. Would have been better if python 3 was used. Please update and provide everything in python 3.

автор: Tomáš Zeman

Nov 30, 2018

Nice intro but it could have been more in depth, not just as a simple graphlab/turicreate tutorials