Chevron Left
Machine Learning Foundations: A Case Study Approach(으)로 돌아가기

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

9,100개의 평가
2,171개의 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


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


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.

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 2,090개 리뷰 중 176~200

교육 기관: Christopher A

Oct 17, 2015

I really liked the case study approach to the topic. The instructors' approach to teaching through the Python notebook made it easy to follow and see things implemented as you learned them. In addition, they presented the material at a good level - not too general not too detailed for an intro taste to the topic. The professors were engaging lecturers as well and I found myself quickly going through each week's content to get to the the enjoyable assignments. I'm excited for the other courses in the Specialization.

교육 기관: Stefan K

Dec 28, 2015

Very good introduction to machine learning, the teachers and assignments are very well planned and executed. It is a course where you can spend more time, as the workload is bigger than at usual courses, but you learn a lot every week. I am really fan of this Specialization and I plan to complete the whole Specialization given enough time in near future.

교육 기관: Easton L

Feb 25, 2017

Emily and Carlos are really exciting teachers. This course covers fundamental concepts of Machine Learning and comes with very practical assignments. I've learned a lot from the this course and I believe it will make me ready for more challenging work in the future.

교육 기관: Deepak

Aug 24, 2016

This course gives overview of what we are going o learn ahead in machine learning course. Carlos and Emily they both explain stuffs in very detail manner. IN fact it so much fun to learn when you understan thing and specially these cool stuff i hope to see some more courses on this in future. :)

교육 기관: Mohammad P

Mar 08, 2017

Exceptional, my best course in Coursera

교육 기관: Shihab H

Oct 31, 2016

Very easy to follow, well-paced, practical / not just theory, cheesy jokes :)

교육 기관: Mayuresh W

Nov 23, 2015

The course was well detailed and gave a good idea of what to expect when learning about machine learning and this specialization.

Covering each of the topics well with sufficient explanation and a small project was a great way to learn.

Looking forward to the next courses :)

교육 기관: Patrick F

Jan 15, 2016

The course offers a great practical approach using sophisticated software. As a participant, I gain valuable knowledge on a wide range of applications.

교육 기관: Hari S

Sep 25, 2016

Loved the course. Hope to do more soon.

교육 기관: Javier F

Mar 20, 2017

simple, self explanatory and self enought to start playin with machine learning

교육 기관: Kowndinya V

Mar 04, 2018

This course provide a very intuitive explanation of different machine learning models. It also has a good blend of hands on programming. Especially the combination of python notebook and graphlab give a unique experience. The visualizations from graphlab are amazingly good. It's really additive.

I would like to Thank Emily and Carlson for their great work in putting the right level of content for this course keeping the audience in mind. I feel bottom of my heart that I could really learn something significant and meaningful.

Overall, I must say it was an awesome experience.

교육 기관: Purbasha C G

Dec 04, 2016

Great Introduction to machine learning. Found the Turi APIs and iPython Notebook approach very effective in getting acquainted to machine learning algorithms.

교육 기관: Michael L

Dec 24, 2016

what an awesome mooc. incredibly practical, consistent, coherent and eye-opening! looking forward for the next courses in this specialization.

교육 기관: Hassan F

Feb 08, 2016

Great overview of basic ML concepts in different situations along with hands on exercises. It was really helpful, with examples and little programming challenges that help learn easily.

교육 기관: Saravanan C

May 26, 2017

The strategy of making people to become comfortable by first providing simple hands-on and building confidence is good. That will motivate them to stay along ALL the 6 course without getting perplexed. I see good team work! Thanks - I will strive to complete all the six modules.

교육 기관: Nicholas S

Oct 07, 2016

Great course.

교육 기관: Jose N N P

Mar 30, 2018

Excellent course and very challenging, most importantly, I have learned a lot and I have a great understanding of what machine learning is. Dr. Carlos and Emily are great instructors, and indeed engaging as well as passionate. Looking forward to taking the next one.

교육 기관: Bei l Z

Feb 05, 2016

Very good introductory course. Doesn't require good depth of programming languages. Gives a good overview of ML and data concepts.

교육 기관: Alexander S

Feb 07, 2016

great and funny.

교육 기관: William D C

Oct 22, 2017

Fantastic course, great 'learn-by-doing' introduction to ML, really entertaining teachers kept me alert throughout each session. It was great fun and I learnt a ton!

교육 기관: GAURAV B

May 27, 2016

Simplistic approach taken to make understand a complex subject.

교육 기관: Enrique C M

Dec 06, 2016

Brief but very good overview to typical Machine Learning models that are currently being used in many real applications. Nice and easy going teaching model based on case studies and lots of examples and practice during the assignments. For being only an introduction to this world, it was a quite interesting intro and now I am keen to follow up with the next parts of the specialization.

교육 기관: Chen Y

Feb 18, 2016

It's a neat course

교육 기관: Luis M I M

Feb 16, 2016

The way all the topics are introduced is great. The assessments are simple but its approach to real problems keeps one interested.

교육 기관: Chen Q

Oct 04, 2016

Clearly course, but still hope to get more information about how to use other frame work