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Avis et commentaires pour d'étudiants pour Machine Learning Foundations: A Case Study Approach par Université de Washington

4.6
étoiles
13,082 évaluations

À propos du cours

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....

Meilleurs avis

PM

18 août 2019

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.

BL

16 oct. 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

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426 - 450 sur 3,043 Avis pour Machine Learning Foundations: A Case Study Approach

par Hemanth P H

24 oct. 2020

A good course for getting a rough idea of what machine learning is, and what its applications are.

The course provides the basic knowledge for the specialization course. Overall I enjoyed it thoroughly.

par uday s

1 août 2017

Great curse for anyone wanting a high level over view of machine learning. Had good mix of tutorials and quiz as well as homework. The faculty were very good and kept up the interest with some good hum

par SAI C P

4 nov. 2016

The course touches upon all the necessary fundamentals of machine learning in the best way possible -- through case studies. One gets a hang of the ipython notebook and graphlab create environment too.

par Revanth P

16 juin 2016

Great course to get an overall view and some hands-on experience for Machine learning. Thoroughly enjoyed working the course. The videos are short and concise, making the learning experience enjoyable.

par Lancy

26 oct. 2015

Liked the approach to use case studies to explain Machine Learning and the many algorithms. Brought the otherwise complex subject to life. The hands-on exercise were very useful to consolidate learning

par organek.natalia@gmail.com

23 avr. 2020

I really enjoyed this course. Even if it is only about foundations I feel I understand ML better and I knew almost nothing about it in the beggining. Thank you for your work to give as such courses :D

par novajo

5 déc. 2017

definitely 5 stars. I would like to say this course is much more closer to the beginner (including myself) who may not have sufficient concepts and experiences on the machine learning, linear algebra.

par chris l

8 oct. 2019

This was good introductory course with challenging programming assignments that expanded and grounded the lecture materials. The forums also proved great support when needed, overall very satisfied.

par Munesh S C

5 juil. 2020

A very balanced first course that introduces machine learning in a very practical and simple way. I would recommend highly this course to anyone who plans to learn machine learning through practice.

par Dipanjan S

28 sept. 2015

Excellent course, with really good lectures, material and assignment. Plus the professors are really amazing and their enthusiasm is really refreshing and makes the class more interesting. Loved it!

par AYUSHMAN H

28 mars 2022

very nice course.If you have basic knowledge of python datastructure then this course is best to start data science.All contents are beginner friendly which makes this course easily understandable.

par KARTHIKEYAN K

11 févr. 2019

The course module is very clear and very useful for me to understand the ML concepts.

Really excited about more features in the C_Stone project where i think we can do something for my organisation.

par Rebekah H

9 juin 2017

I felt this course did a good job introducing the student to Machine Learning. The examples and hands on assignments brought the concepts home. I was able to use the knowledge immediately at work.

par Govindarajan

4 juin 2017

This course is very helpful for people who are novice in machine learning. The course uses Graphlab Create which is different from scikit or R-libraries, but the tool(Graphlab) is excellent to use.

par Bhavesh G

28 mars 2020

This course foundation for those who want to do specialization in Machine Learning. It's really very useful course, I recommend do this course If you want to do specialization in Machine Learning.

par geetika s

8 nov. 2016

One of the best courses available online. Actually got to know how to apply theoretical knowledge in designing systems. You people are the best and made concepts and things really easy. Hats off!!

par Ashley A

14 nov. 2016

Really liked the course and the teachers. Would have preferred more detail on the quizzes so I didn't feel as lost as I did some of the times while trying to piece together what a question meant.

par Chandrabhan

21 juin 2020

I'm very thankful to coursera. It's provide a cost of free certification of machine learning which cost in market is approximately 3000rs.i think coursera is a good platform. Thank you coursera.

par Sarim A

7 oct. 2017

really like the instructor and the course. it was very hands on specially for me who is coming from Bigdata (python and hadoop) background . thanks for this cool and amazing learning opportunity

par Amr H

20 juin 2018

The course Content is very good and also the instructors .graphLab tool is also good toolI wish there was a hint for scikitlearn but it is a good course for beginners and i Recommend it for all

par Jesse C

28 août 2021

R​eally enjoyed all the material presented by the professors! They're enthusiasm for machine learning is contagious. I would highly recommend this course as an entry-point to machine-learning.

par Naveen T

24 avr. 2016

Excellent course! I like Emily and Carlos' approach to delivering online courses and the content and structure of this specialisation. I would definitely recommend this course to my friends.

par Joseph K

29 oct. 2015

Great survey course for main topics in machine learning without going too much into detail. The professors do a great job of keeping the topics relevant to modern-day uses of machine learning.

par Prakash M

18 févr. 2022

It's very important to attend this course to understand basics of ML and how we can approach further.

It's really helpful for me to understand and go ahead on deep dive into the ML techniques.

par Nasir M

12 août 2020

Excellent foundation course on ML, Enhanced the wish to learn detailed topics in ML, very attractive methods by Instructors, Thanks for creating thirst and encourageuing to learn more on ML