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워싱턴 대학교의 Machine Learning Foundations: A Case Study Approach 학습자 리뷰 및 피드백

4.6
8,951개의 평가
2,143개의 리뷰

강좌 소개

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....

최상위 리뷰

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.

PM

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.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,062개 리뷰 중 251~275

교육 기관: OG

Dec 29, 2015

Great overview with some implementations of what will be covered through the specialization.

교육 기관: MD O F

Dec 04, 2016

Very good course for quick learning !!

교육 기관: Alfredo A M S

Jun 27, 2016

Good overview of ML methods combined with a gentle introductions to Python and iPython notebook

교육 기관: Giovanni C A

Nov 03, 2015

Excellent

교육 기관: Chenxi W

Mar 22, 2016

Really good.

교육 기관: Rajat S B

Mar 17, 2016

Great, It clears what we will learn and what our approach should be during the specialization.

Thank you

교육 기관: Ranu

Feb 23, 2016

Looking forward to learn more.

교육 기관: Eden W

Mar 23, 2016

Fantastic! Love the professors.

교육 기관: 黄怡

May 30, 2018

Actually, this course is the best introduction for machine learning for me .

it gives me a outline of machine learning structure . thankful , and i will continue learn other courses in this whole course .

교육 기관: Angel G C

Dec 13, 2015

In a couple of Case Studies it gives you a wide idea about the almost unlimited potential of Machine Learning while it encourages you to learn more and more about it.

교육 기관: Evaldas B

Nov 03, 2017

Great basics but a shame that python2 is still used in the course. Also graphlab is not the latest thing in the market. But overal very good course

교육 기관: Sergey T

Jan 03, 2016

It was instructive, easy to follow and fun to learn! Great thanks to Carlos Guestrin and Emily Fox for creating this excellent course! And thanks Coursera for making the high quality educational content available to everyone.

교육 기관: Daniel C

Feb 10, 2016

Presenters start off kind of silly and made me wonder what I was getting into. However this class quickly evolved to be 100 times better than the course offered by U of California on Big Data. You do actual python programming through a lot of serious concepts in data analysis, visualization, and machine learning. This first course is hands on - just use the libraries. They lean heavily towards Dato which is not open source - using a 1 year trial license. However there are better instructions and support for open source in subsequent courses. Also - the second course in the series which I'm taking now is taking what we did in course 1 and diving into the math and algorithms involved - walking through actual proofs etc. It doesn't require you to know them well enough to do on your own, but they do walk you through them and explain extremely well - you actually implement the resulting algorithms. I'm fascinated by this course and can't wait to apply what I've learned.

교육 기관: Mukul P

Dec 07, 2016

Exceptional introduction to all of the concepts involving Machine Learning! Highly Recommended!

교육 기관: Kumar N

Feb 14, 2016

Awesome for beginners

교육 기관: Maxwell N M

Dec 12, 2015

Very Cool

교육 기관: Siva J

Feb 22, 2016

A very well designed course. This course clearly sets a path for introducing basics and fundamentals in a way that makes it challenging but insightful. It establishes the purpose for more advanced courses in this specialization and the need to dive deep in this fascinating subject (machine learning).

After completing this course alone, I can say categorically, there is no turning back, there is no sitting on the sidelines, there is no learning something peripheral. Doing this entire specialization will make any successful learner a very successful contributor in the world of Machine Learning

교육 기관: Philippe L V

Nov 26, 2015

This course is very clear and gives an intuitive idea of what is machine learning. It is also very practical with python implementations for each chapter.

교육 기관: Usman I

Oct 24, 2016

For me, this course excelled at brushing up ML concepts I had studied years ago and clarifying the appropriateness of different techniques for different problem settings. However, the best part about this course, and the reason I took it in the first place, was that it introduces participants to a new tool that is scalable for use in larger / production systems.

I am much obliged to the instructors and am sure to continue on to the next course in this specialization.

교육 기관: Apurva A

Mar 02, 2016

Excellent Course!

교육 기관: Zachary N

Dec 13, 2015

Great overview of machine learning techniques and practices at a high level! There is sufficient material here to go from no machine learning knowledge (and a general programming background) to being able to create and deploy machine learning models for use in applications.

교육 기관: WEI Y

Jul 05, 2018

Great course!

교육 기관: veneshkumar

Apr 01, 2017

excellent course

교육 기관: Daniel V

Mar 27, 2016

An easy, not simple, but humorous approach to a broad topic with practical samples that you can build on for further studies. Good for newbies as well as a fresh up for advanced applicants. Looking forward to the follow up courses.

교육 기관: Samriddh K

Jun 15, 2016

It is a good introductory course to machine learning