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Learner Reviews & Feedback for Machine Learning Foundations: A Case Study Approach by University of Washington

9,102 ratings
2,172 reviews

About the 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


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


Aug 19, 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.\n\nThe forums and discussions were really useful and helpful while doing the assignments.

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101 - 125 of 2,091 Reviews for Machine Learning Foundations: A Case Study Approach

By sohan l b

Mar 04, 2019

This course is good for beginners.This course covers hands-on as well as Basic theory and their applications .

By Lahiru H P

Mar 04, 2019

great course content to get started with machine learning and also for deep learning.

By Akash G

Mar 08, 2019

START basic like star

By Evan S

Mar 11, 2019

This course was a great balance between lecture (and lecture quiz) & iPython lecture (and iPython lecture quiz). I like that the answers are multiple choice as opposed to copying and pasting code. That way, any coding errors can be played around with in the notebook first without using up any submission attempts. Emily and Carlos did a great job of keeping the course fun while sticking to the easy-to-understand case-study approach.

By Jose E S S

Jan 13, 2019

Awesome, better course of machine learning.

By Md. R K

Dec 14, 2018

Awesome course to get started to ML with Python.

By Shakya S B

Dec 28, 2018

This course is very helpful for a beginner and provides a good foundation for the specialization and the advanced courses

By Yamin A

Dec 30, 2018

Excellent introductory course on Machine Learning. The material is taught at a level that does not require much in terms of pre-requisites, both in terms of the math and the programming requirements. From my perspective, I have an extensive background in Math, and some background in programming (MATLAB, R). I had not used Python prior to this course, and I found that I could keep up and learn both some Python and ML. I was able to finish the course in two weeks. Well done to the instructors who made the videos fun and accessible. Recommended for anyone who wants to learn something about ML.

By 宁莽

Dec 15, 2018


By Abhishek B

Dec 16, 2018

Good Machine Learning course for beginners.

By Jithesh R

Nov 30, 2018

Great Experience...! Loved it...!

By Praveen k

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.

By Abhishek P

Jul 31, 2018

Very good course

By Fakrudeen A A

Aug 05, 2018

Excellent course and highly recommended - covers fundamentals, TF-IDF, cosine. jaccardian similarities, recommender systems (precision/recall, AUC), deep learning via transfer learning (not having to explicitly build a model for the problem).

Exercises could be done in some tool which is common across industry.

By Anunathan G S

Aug 05, 2018

Lucid over

By Pooja G

Aug 07, 2018

Loved the course content. Particularly loved the usage of iPython notebook. very relevant & useful. Special thanks to the course instructors for helping guide through the course.

By Yashaswi P

Aug 06, 2018

I still think it is better to have different course content at the start for programmers, people with some mathematics background and others

By Rohan C

Jul 19, 2018

Emily and Carlos made this course really enjoyable. The Case Study approach really helped with better understanding of many concepts. I highly recommend this course for beginners.

By Pritesh G

Jul 20, 2018

Good material. Enjoy the Course.

By leonardo d

Sep 01, 2018

Well thought methods for modern data analysis

By Basha S

Sep 05, 2018



Aug 23, 2018

This will be really helpful for someone who really wants to start the ML journey and not sure where to start. The content was designed well to suit people across levels and technologies. Strongly recommended.

By jorge j l c

Sep 05, 2018


By Leonardo M d O

Aug 25, 2018

Amazing course. I had already done other ML Courses at coursera, but the competitive differential is the friendly approach took by the professors. Carlos and the other girl are very nice, they the training gets less formal, they look like a friend telling stories in a bar. Another main point is really the uses cases. They swap between the big forest map and the detailed view of the leaf in a succinct way. Easy to understand both views. Congratulations.

By Roma A

Aug 25, 2018

Good intro to ML