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

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

4.6
Sterne
11,909 Bewertungen
2,851 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....

Top-Bewertungen

SZ
19. Dez. 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.

BL
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

Filtern nach:

2676 - 2700 von 2,770 Bewertungen für Machine Learning Foundations: A Case Study Approach

von Vamshi S G

27. Juni 2020

i think the course should be updated, graphlab and some other are outdated.

von Julien F

16. Nov. 2017

Some quiz questions were vague and/or ambiguous, or not covered in talks.

von Marco M

4. Dez. 2015

Too much synthetic on very important parts, too much focused on graphlab

von Diego A A V

12. Nov. 2020

TuriCreate is not the apropriate tool for practical Machine Learning

von Pawan K S

15. Mai 2016

Nice introductory course but too much dependence on graphLab create

von Jesse W

24. Dez. 2016

It is better if allow me upgrade only when I finished this course.

von Tushar k

30. Nov. 2015

Good course to begin machine learning with but it's too easy !!

von Konstantinos L

8. Jan. 2018

Nice course but too easy. Assignments should be more difficult

von Atharv J

14. Sep. 2020

The course should be taught in pandas rather than graphlab.

von Max F

10. Jan. 2016

Not a bad course, but extremely basic. Was expecting more.

von Adrien L

2. Feb. 2017

No good without the missing course and capstone projects

von Himanshu R

16. Apr. 2020

It uses turicreate which is not available for windows .

von Aleksey C

11. Dez. 2016

....mmm fsdfg gsgsd sgsdgsdg sdsdgsdg ggsgsd sgdsdgsg

von HITESH D

15. Juni 2020

Installing software parts gave me a very hard time.

von Bastian M P

1. Juni 2016

Could go a little more in detail on the algorithms.

von Jaime O

31. Jan. 2017

The Deep Learning part needs to be improved

von Chen S

26. Okt. 2015

Very basic, the quizzes aren't clear enough

von Li-Pu C

29. Okt. 2020

A little bit too easy, but good for rookie

von Harsh V K

8. Mai 2019

Should use Python 3 instead of Python 2

von Jorge A C C

29. Mai 2016

It is a very simple course.

von RAGHUPATHI R R

25. Juni 2020

Good for knowledge

von Fredick A S

6. Apr. 2018

No python..

von Nasimul J F

16. Aug. 2020

THANK YOU.

von Kai C

24. Nov. 2015

Too easy

von JONNALAGADDA A

12. Sep. 2020

good