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

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

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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


2019년 8월 18일

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.


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

필터링 기준:

Machine Learning Foundations: A Case Study Approach의 3,043개 리뷰 중 451~475

교육 기관: Aradhika N

2017년 6월 21일

Love how the modules are broken down into small segments of 3-5 minutes on an average. Makes it easier and definitely not monotonous as compare to other courses. The professors are amazing!

교육 기관: Mahmoud A E

2016년 2월 28일

The top-down approach of this course is the best way to understand concepts and view solutions for real-world applications. This way I can go deeper after understanding why I am doing this.

교육 기관: Nagendra K M R

2018년 9월 22일

Explanations are provided in detail which helps even the beginners to master the Machine Learning. Case studies are very interestinghelpful to master the concepts and gain the confidence.

교육 기관: Robert R

2018년 3월 25일

A running Jupyter notebook with working examples. Very nice. I couldn't get my local system setup the way they explained, probably because my Python is 3.x is newer than 2.x. Not sure.

교육 기관: Dauren

2017년 12월 22일

Gives a good overview of tools and models used in Machine Learning. Once taken this course, you will have a general knowledge of domain upon which Machine Learning methods can be applied.

교육 기관: Ramy S

2019년 6월 22일

Excellent course. I am currently working at and find that this is a perfect supplementary course that will allow a professional to solve business problems. I highly recommend.

교육 기관: Joseph L

2016년 2월 28일

Had a blast. I have no background in ML whatsoever. But the tools, concepts and exercises presented is really interesting and really help set the mood for the rest of the specialization.

교육 기관: Rogelio Z R

2015년 12월 3일

Emily and Carlos are amazing! The course is well laid out, specially as part of the specialization, taking the regression course would have been different without the foundations course.

교육 기관: Francesco P

2021년 3월 16일

Nice introduction to DL, easy to follow with the suggested turicreate or any other framework.

IT is juts a pity that the specialisation this course belong to will no longer be completed.

교육 기관: Prabuddha K

2017년 4월 2일

Brilliant overview. Many thanks to the teachers for designing such a comprehensive overview. This course must be followed by all the others in the specialization for best understanding.

교육 기관: Richard K

2016년 12월 16일

Great course, really well designed and with some interesting real life case studies. Lectures are clear and informative and the assignments help cement your understanding of the content

교육 기관: James P

2016년 11월 27일

Very nice overview / introduction to machine learning. Setting up the environment initially was annoying but well worth the effort to be able to analyze/solve more realistic use cases.

교육 기관: Hassan F

2016년 2월 8일

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: Bilong C

2015년 12월 29일

This is very great course to get students introduced to different machine learning algorithms before digging into the details. And the Graphlab used in the course is really easy to use.


2022년 1월 18일

Very good, thorough and helpful! has a good mix of theory and practice. However, the assignments can often be tough to complete and often need extra reading and/or knowledge to tackle.

교육 기관: Jay

2020년 8월 15일

great starter course to dive into machine learning. it gives you some idea on type of the problem that ML can handle. not much details, though! they are left for the following courses.

교육 기관: Neha R

2017년 5월 25일

It's a really good course and covers all the basics extensively.

It is well structured and the case-study approach actually helps understanding the topics in a better manner and easily.

교육 기관: 龙腾

2016년 7월 1일

It's an very interesting and intuitive course. But using the Graphlab Libarary require more CS background. this course should add more document and instructions on how to use Graphlab.

교육 기관: Kim K L

2015년 12월 11일

This is a really really great course ... and that the professors appear to really enjoy teaching and are fun fun to watch and learn from is an additional bonus. Keep up the great work!

교육 기관: Barış D S

2016년 2월 14일

Great course, great framework, thank you. But in my humble opinion, the lecture videos are too short. Lectures are generally divided into several videos, covered a lot of transitions.

교육 기관: 向韵桦

2016년 1월 31일

It's really helpful to pull back and have a overall look at these algorithms. Especially, the professors gave a very clear talk and explanation which made this course more impressive.

교육 기관: rambarki g

2018년 3월 11일

This was a awesome moment for me it was really cool. The people of course era i love them .Thank you so much for financial aid. Keep supporting people like thank you thanks a lot!!!!

교육 기관: Wenxin X

2016년 2월 25일

In my opinion, the course is well designed. I generate a rough idea about the basic concepts of machine learning through it. These concepts are important but made easy to understand.

교육 기관: Jerome G

2015년 12월 28일

Excellent overview of machine learning technique !

Even if the subject is complexe, it's easy to understand, and a good starting point to go deeper, as a deep human learning can be ;)

교육 기관: Udaibir S B

2020년 5월 11일

The course was up to the mark, the quality of the assignments and quiz was also good which created the course more interesting to learn and learned many new things with this course.