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2,858 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

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

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.

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2501 - 2525 von 2,772 Bewertungen für Machine Learning Foundations: A Case Study Approach

von Mehul P

1. Aug. 2017

Nicely explain use case of ML.

von Jijo T

6. Okt. 2015

I love the hands on exercises.

von Mazen A

9. Okt. 2016

the best introduction for ML.

von Rishabh C

23. Juli 2020

Awesome course to start with

von Rakesh G

15. Apr. 2019

A good beginners guide to ML

von RISHAB P H

15. Apr. 2020

add more practical's please

von Mahesh B

10. Okt. 2019

Good start for ML beginners

von Poornima S

18. Feb. 2019

It is designed really good.

von Hyeong R J

2. Feb. 2017

Good lecture and practices.

von Marcos M M

24. Aug. 2017

Great introductory course!

von SUPRIYA V S

30. Juni 2018

Nice course for beginners

von Vinicius G d O

23. Juni 2016

Good introductory course.

von José T G R

1. Nov. 2015

Very good!!! Excellent!!!

von Tushar A

13. Juli 2020

This is a nice course..

von Fernando S

20. Aug. 2017

Easy going, very good!!

von Godwin

4. Juni 2017

Very interesting :) WOW

von Annie I R

4. Jan. 2016

This is a great course.

von Mayur S

18. Jan. 2017

its good, if new to ML

von Wridheeman B

30. Juni 2020

It was a great course

von Eric S

5. Jan. 2016

Pretty good, overall.

von Mahajan P J

26. Dez. 2019

The course was good.

von Richik G

11. Juli 2019

computer vision best

von Pieterjan C

2. Okt. 2017

very useful to start

von Shreeti S

16. Aug. 2017

Good to start with.

von Waquar R

8. Aug. 2016

this is really good