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

4.6
별점
13,053개의 평가
3,105개의 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

BL

2016년 10월 16일

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

PM

2019년 8월 18일

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 3,032개 리뷰 중 501~525

교육 기관: vivek m

2017년 3월 3일

Best course to get start with ML as it has lot of real world example to get your hand dirty, which will help us to develop approach 'how to solve real world problem using ML '

교육 기관: Farouq O

2016년 2월 3일

The course did a good job of balancing depth with breadth. It's a well rounded course that provides a a student with enough information to tackle intermediate-advanced topics.

교육 기관: Alexander B

2015년 12월 12일

Very best initial level course that will introduce anyone to one of the modern ml tools and its usage, with a bit of needed theoretical science (its only an approach aint it?)

교육 기관: Sagar S

2020년 6월 7일

This is a very well designed course to build the Machine Learning Foundations for any level. And also its a perfect segway to remaining detailed courses of the Specialization

교육 기관: Shah H

2019년 12월 6일

Enhance my knowledge in ML and skilled me to do best Research in my MS Study, Thanks to COURSERA and University of Washington to give financial aid to learn Machine Learning.

교육 기관: Parth P

2018년 4월 1일

Hey This is Excellent course for beginners. The homework assignments are designed to grasp concepts easily and in most practical way possible. Thanks for such a great course.

교육 기관: VITTE

2018년 3월 11일

Very interesting, useful, and up to date, this course gives the main ideas with clarity, and relevant applications, in a time format that is feasible for an active engineer.

교육 기관: Dheeraj A

2016년 10월 28일

Course combines Real Word Applications with simple implementation via IPython Notebooks. Professors

know their stuff but are super chill. Pretty good assignments and quizzes.

교육 기관: Scott W

2016년 6월 10일

Great way to warm up the class. Seeing how the various techniques and best practices should/can be used was very helpful in warming up for the more densely focused classes.

교육 기관: Omri R

2016년 2월 29일

This is a great intro to a range of topics in machine learning. I do recommend pursuing the entire specialization since this course only scratches the surface of each topic.

교육 기관: Marcus C

2016년 2월 8일

great course. This covers all types of machine learning techniques deep enough to get a basic idea how things work. Enjoyed a lot. Instructors are really fun to learn from.

교육 기관: Cissy S

2015년 12월 2일

Loving it so far! Can't wait for the other courses. The case study approach is spot on! This is the first coursera course that is worth something! Kudos to the instructors.

교육 기관: Pankaj K

2017년 9월 25일

Nice overview to ease into all the content!, Only bad this is they use sframe :( either make it opensource and in the mainstream use or provide the assignments in sklearn!

교육 기관: SMRUTI R D

2016년 2월 15일

A very informative beginners cource which offers a macro view of different approaches to MachineLearning and prepaes the student for further study in each different areas.

교육 기관: Kirill L

2016년 2월 3일

Great for a start.

Still has some issues for those who use sklearn and pandas.

Also I'd prefer to see more detailed info on neural networks instead of deep learning module.

교육 기관: Fabian d A G

2021년 8월 15일

E​xcellent course that spans the broad ML domain. Unfortunately it appears that the specialization ends sooner than what was planned, but remains quite good nevertheless.

교육 기관: Vishal A

2017년 11월 29일

They have used graphlab instead of using standard library. But overall good course.

If the student can submit quiz question without enrolling then it would be a big plus.

교육 기관: Amy M

2017년 7월 11일

The instructors were fantastic, the material was understandable, and the reach I have beyond this course is still expanding. Thank you for a wonderful learning experience

교육 기관: Dan S

2016년 3월 13일

I found this course as a great introduction to the world of machine learning with a very practical approach.

I'm waiting forward to the next courses in the specialization.

교육 기관: Thomas K

2016년 3월 5일

I loved this course! It is really well done, that you have a theory part and a practical "case study" part, were you can follow along with the provided IPython Notebooks.

교육 기관: Eric A J C

2021년 4월 17일

An excellent course to get introduced to the field of Machine Learning. Amazing professors! And actually putting the knowledge into practice with Python was a big bonus.

교육 기관: NITISH C

2019년 9월 10일

This course is designed in a very planned way. It gives you a bird's eye view of the ML world without boggling down you with too much technicalities. Highly recommended.

교육 기관: Muhammad U C

2016년 2월 11일

A good theory and practical based overview of major machine learning tasks. Hands-on practice using GraphLab Create makes it one of the best courses in Machine Learning.

교육 기관: Sanath K S

2020년 8월 5일

An immaculate course to get your basics and facts right to get a head start in ML.The mentors did their part well. I strongly recommend the vert course to the aspirants

교육 기관: Deveer B

2019년 12월 22일

Very good theory and practical approach. However, some of the assignments are not very clear while explaining the questions due to which sometimes we get wrong results.