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

11,515 Bewertungen
2,757 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....



Aug 19, 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.


Oct 17, 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:

2301 - 2325 von 2,673 Bewertungen für Machine Learning Foundations: A Case Study Approach

von Ricky W

Feb 10, 2016

Very nice introduction to Machine Learning and to Python programming language

von Daniel B S d S

Nov 02, 2016

The course is great, but it would be greater if used open source free tools.

von Bilal S

Oct 17, 2016

It' a fine beginner's course. I liked the hands-on approach using SFrames.

von Marco P

Dec 04, 2015

The homework assignments were not really about having understood the course

von Sourabh K

Jun 30, 2020

numpy and pandas are more preferable, but the overall experience was good.

von George B

May 17, 2018

Pretty great course. Really enjoyed it and looking forward to new courses

von Jeffrey v S

Oct 31, 2017

Content is good but the delivery is somewhat awkward and chatty at times.

von Brennan W

Feb 04, 2017

Was a good intro to different kinds of ML. Wish we had used SciKit-Learn.

von Nandan S

Mar 15, 2018

very good overall. The last week (Neural networks) is a little too fast.

von Ramesh S

Mar 14, 2018

A good and quick introduction to ML. Like the Case Study based approach.

von Anastasiia

Feb 02, 2018

OK course if you don't have any background knowledge. Graphlab oriented.

von Aaron M

Jul 02, 2017

Seems a bit old but it was a great way to introduce myself to the basics

von Matías G

Oct 08, 2016

Great Course, just felt little weak the last module about deep learning.

von Xiaosong L

Dec 18, 2015

a good introduction of the topics. I like the ML diagram in each module.

von Lucia d E P

Feb 05, 2018

I enjoyed the course and the fact that it uses Python for the exercises

von Xavier H

Aug 08, 2016

A good introduction tot he tools and possibilities of machine learning.

von Zhe W

Oct 27, 2015

Useful course to get general idea to get onboard with Machine Learning.

von Leon

Oct 01, 2019

Goes through many topics, but not as in depth as one would have liked.

von Jacques J

Sep 08, 2017

Was so good to get some exposure to the different areas of application

von Sandeep K S

Jan 05, 2016

Good course with the overview of different machine learning techniques

von fredfoucart

Dec 10, 2015

A good global introduction and simply explained. With fun as well....

von Ali N

Nov 13, 2015

Really great course content, but the assignments could become better.

von Harshal M

Aug 18, 2017

Great Course!! Helped me learning new things. Great way of teaching.

von federico w

Apr 04, 2016

Great course. Super case driven approach, professors are very clear.

von أحمد ج

Aug 06, 2019

wish to use more common ML libraries, but the content was very good