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

4.6
8,034 ratings
1,961 reviews

About this Course

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....

Top reviews

By SZ

Dec 20, 2016

Great course!\n\nEmily and Carlos teach this class in a very interest way. They try to let student understand machine learning by some case study. That worked well on me. I like this course very much.

By 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

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1,884 Reviews

By Sameh

Jan 20, 2019

very good and very nice course, it added lots to me

By akashkr1498

Jan 18, 2019

lacture was good but one point i want to share to you don't use rare tools for assignment personally i faced lots of problem while installing graphlab better to switch to some common tools like sklearn python platform .

By Mallikarjuna Rao V

Jan 17, 2019

Wonderful opportunity to learn and execute hands on coding of Machine Learning. The amazing task that Machine Learning methods and algorithms does behind scene is understood for the following cases / intelligent applications:

1. Regression (e.g. Predicting House Price etc.)

2. Classification (e.g. Product review sentiment, Spam detection, Medical diagnosis etc.)

3. Clustering and Similarity (e.g. Grouping news articles)

4. Recommender (e.g. Amazon personalized product recommendations, Netflix personalized Movie recommendations etc.)

5. Deep Learning and Deep Features (e.g. Google image search, Image-based filtering etc.)

The main challenge for me was to code using “Python3, Pandas and SciKit-Learn” instead of “Python2, GraphLab Create and SFrame”. I am now confident to develop intelligent applications based on Machine Learning. Thanks to Professors (Emily and Carlos) and to Ashok Leyland-HR for giving me this opportunity.

By Swapnil Sudhir

Jan 16, 2019

This course is pretty good for someone who wants to learn how to implement machine learning models, how to train data and how to make the most out of machine learning. The only issue i found is they are using GraphLab a lot more where as SciKit learn is the industry standard. Overall, its a good learning way, models and other things you can learn on your own once your basics are clear. Great Course.

By Jose Eduardo Santarem Segundo

Jan 13, 2019

Awesome, better course of machine learning.

By SIVASHANKAR S

Jan 11, 2019

The fundamentals of coding and machine learning concepts are taught in such a way that even a person with no background in computer science can grasp easily.

By Wilfrid Lemasson

Jan 08, 2019

Very good course, I enjoyed the way the instructors structured and presented the material, in both a professional and personable manner, and the use of case studies to help solidify the knowledge. Assignments were very well built; although they used quizzes, it really required some thinking and prep work to get the answers right.

By Artem

Jan 08, 2019

awesome course with theoretical and practical knowledge

By faiza Sharif

Jan 07, 2019

Good as theoretical concepts but labs stick with Graphlab which not commonly used library. But overall good experience.

By Rania Benman

Jan 06, 2019

I had to use TuriCreate instead of GraphLab, so other than the changes in the libraries that had me guessing which function to use, everything in this course is well structured and concrete. Thank you all!