Chevron Left
Zurück zu Machine Learning Foundations: A Case Study Approach

Bewertung und Feedback des Lernenden für Machine Learning Foundations: A Case Study Approach von University of Washington

13,055 Bewertungen
3,105 Bewertungen

Über den Kurs

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....



16. Okt. 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


18. Aug. 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.

Filtern nach:

551 - 575 von 3,033 Bewertungen für Machine Learning Foundations: A Case Study Approach

von Ali A

21. März 2016

This course builds a quick but engaging overview of machine learning. The structure is more than amazing, the style of teaching is very narrative and very helpful.

von Liliana R

22. Jan. 2016

It is a great course to learn the fundamentals of Machine Learning, also Emily and Carlos are excellent tutors, they explain very well and they give good examples.

von Vinícius N d O

6. Okt. 2020

Good course. Strongly application-oriented, intermediate level but with some basic points as well. I think the course could improve a bit on the theoretical side.

von Gyrdymov I

12. Nov. 2017

Amazing course. It has given me good base knowledge of ML algorithms, I consider the course a "map" for further exploration of Machine Learning world. Good start!

von Marcus V M d S

1. Okt. 2017

Thank you very much for this course! I really appreciate the effort put into the notebooks and the preparing of the data, and the graphlab library is really cool.

von Guo X

10. Aug. 2017

the course is great. I love case study approach for the machine learning foundation. I really love to go on learning in the following parts of the specialization.

von Ricardo J F

17. Sep. 2016

I believe it would be very useful if the authors could provide a lecture review about python commands. Specially the ones they are going to use during the course.

von Christopher G

25. Jan. 2016

Excellent, practical introduction to the topic -- particularly by using use cases that everyone can relate to in order to understand how machine learning "works".

von Hui Z

4. Mai 2020

Carlos and Emily provide excellent lectures. But it would be better if correct answers and detailed explanation can be provided for each assignment in the course

von Adarsh K

10. Apr. 2020

course and the instructors were very good.

But figuring out how to install all the softwares in windows using wsl and copying files into that was a little tricky.

von Rohit k

12. Juli 2019

On behalf of our customers, we are focused on solving some of the toughest challenges that hold back machine learning from being in the hands of every developer.

von Ravi A

27. Okt. 2016

Very good starter course for Machine Learning with enough practice examples. It was really helpful for me to understand what ML is and its real time application.

von Rafael A

22. Feb. 2016

Excellent intro course. Emily and Carlos are really great. Very good balance of depth, practical application, and above all: intuitive, thoughtful explanations.

von Fahim K

22. Okt. 2015

This course cover all the basic fundamentals require for Machine Learning.

Doing Assignment before this were never interesting. I am enjoying the assignment part.

von Diwakar S G

6. Juni 2020

This course is the best course that i have learnt till now. Wonderfully designed and presented.

Thank you to carlos sir , emily mam and entire team of coursera.

von 17 - 4 N B R

16. Mai 2020

The best ever course a student can have on machine learning.really enjoyed the course,thanks for all the efforts put on this course by carlos sir ang emily mam.

von Weichang Z

18. Juli 2017

My favourite machine learning course for a starter, not only about these fundamental knowledge, the hands on practice of Python is really helpful for a beginner

von Andy C

13. Feb. 2016

Best bang for your buck for getting hands on experience with popular machine learning techniques. Just the right amount of difficulty for an entry level course.

von Gireesan N P

8. Aug. 2017

Amazing Course. Recommended to anyone with basics of Python. This is one which gives overall a good coverage on state of the art approaches to machine learning

von Ouanis S

9. Nov. 2016

Excellent course, I particularly appreciate how concepts are introduced with examples to set the terrain for the consequent courses. Will definitely recommend.

von Robert H

5. Sep. 2016

Great introduction to machine learning concepts. I look forward to the rest of the specialization and to developing some of my own methods and supporting code.

von Rishabh A

16. Juli 2020

The course was too good. I really enjoyed it & have learned a lot from this course.

Thank you, Coursera for letting me have this wonderful course.

Thanks a lot.

von Amandeep Y

3. Nov. 2017

I really liked case study approach. I think one will be able to appreciate the application of the Machine learning along with the overview of the ML concepts.


14. März 2017

As a statistician, Excellent introduction to ML!! I can't wait doing the other specializations (regression almost done, clustering and classification on-going

von Purbasha C G

4. Dez. 2016

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