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Machine Learning Foundations: A Case Study Approach, Université de Washington

4.6
8,152 notes
1,983 avis

À propos de ce cours

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....

Meilleurs avis

par 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

par 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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1,905 avis

par Yevhenii Sharov

Feb 21, 2019

Very good course! BIG thank you to authors !

par Poornima Shivarajaiah

Feb 19, 2019

It is designed really good.

par OLZHAS SULEIMENOV

Feb 16, 2019

Feeling still long way to go, at least took very serious track - with challenging assignments and re-learning required tasks!

par Krupesh Anadkat

Feb 15, 2019

Uses very old versions of libraries. Many students are facing issues which remains unsolved. Not recommended to pursue it.

par Alain Coutel

Feb 15, 2019

Technical setup is not easy, but great business cases.

par Rohan Verma

Feb 13, 2019

this has been one of the best courses that I have taken online and the output from this is seriously amazing. It really makes your brain work and the forums make sure you don't get lost. I am definitely going to do the specialization course

par Jefferson Noxon

Feb 13, 2019

A good course, but the tools are a bit dated and it's showing its age.

par Madan Ram

Feb 12, 2019

Good course, It give motivation for people to learn ML.

par KARTHIKEYAN KJ

Feb 11, 2019

The course module is very clear and very useful for me to understand the ML concepts.

Really excited about more features in the C_Stone project where i think we can do something for my organisation.

par Ganesh Kamalakannan

Feb 10, 2019

Learning things with good use cases always lot better. This course really helped a lot to understand machine learning clearly. Throughout this course the explanation of the concepts are so clear and assessments are so intuitive.