Chevron Left
Вернуться к Machine Learning Foundations: A Case Study Approach

Отзывы учащихся о курсе Machine Learning Foundations: A Case Study Approach от партнера Вашингтонский университет

Оценки: 13,055
Рецензии: 3,105

О курсе

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....

Лучшие рецензии


16 окт. 2016 г.

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 авг. 2019 г.

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.

Фильтр по:

2851–2875 из 3,034 отзывов о курсе Machine Learning Foundations: A Case Study Approach

автор: Jonathan O

14 апр. 2021 г.

Pros : You will get a great fundamental conceptual understanding of basic ML concepts and practical implementations.

Cons: Using Turicreate over sci-kit learn and tensorflow

автор: Eric.Wang

10 мар. 2016 г.

I don't like this course , because the homework can not match the lesson. I can not got more messages to completed the homework.

So I will Unregister this courser , Thanks.

автор: Cranchian P

22 мар. 2022 г.

There are still many errors and corrections to be made in practical application part and reading parts.

The conversion from graphlab to turicreate is not complete at all.

автор: Morteza M

20 нояб. 2016 г.

The only reason that I am giving 3 star is the design of the quizzes for each week. The readings are too long and the content of the quiz sometimes gets you frustrated!

автор: Chih W L

19 сент. 2016 г.

Professors are very good , i am really enjoy in this class, but no further discussion about implementing ML algorithm, just call the API to handle the sort of data.

автор: Zhongyi T

9 мар. 2016 г.

The lectures are fine. However the content is way too easy. Another course on Coursera `Mining Massive DataSets` is much better, in the depth and horizon.

автор: Fabio

7 окт. 2018 г.

App needed to complete assignments ceased to function early on - forum / admin did not help to find solution. Otherwise good intro to get started with ML.

автор: Deleted A

5 июня 2016 г.

Generally ok. Towards the end of the course, the lectures could have been a bit more in depth - or provide students with a more in depth reading list.

автор: Kai W

21 нояб. 2015 г.

I think this is an excellent course. I would have given 5 stars if this course is not based on Graphlab which is not affordable to the general public.

автор: Murat O

28 янв. 2016 г.

Gives a really broad overview of ML concepts. Examples (and assignments) use a commercial Dato product called (GraphLab Create). Expect nothing else.

автор: suresh k p

28 июля 2018 г.

Nice explanation of basic ML but I would suggest please provide the practise tool with proper integration.That is a big headcahe in this course.

автор: Paul C

24 нояб. 2016 г.

A solid course, let down by quality issues in the last two modules. I hope these are fixed soon because it would make this a top notch course...

автор: Jawahir M A K

17 июля 2020 г.

It will give you an overview about the ML concept. But to get detail we need to have the specialization course or learn it our self.

автор: Kristoffer H

8 июня 2016 г.

Get ready for a course that assumes you have all the software they use already installed without advanced notice or instructions!

автор: Nouf A

26 мая 2022 г.

s​ome of the video content is old, and some of python functions explained gives errors as it have changed and command is updated

автор: Abiodun M

18 мар. 2018 г.

Very good course; except the bugs in Graphlab with reference to .apply and lambda workers command . This needs to be fixed.....

автор: Corey K

11 мар. 2016 г.

All algorithms were black boxed. It was a nice course on how to use Dato's GraphLab and an overview of ML concepts.

автор: Michael B

2 нояб. 2015 г.

Fun lectures but the coverage is too simplistic. Looking forward to the more in-depth courses in the specialization.

автор: Aleksei Z

16 янв. 2020 г.

Materials from video differ from the web ( in videos graphlab, in materials Turicreat), including home assignment.

автор: Yuliana F N

22 дек. 2020 г.

Me pareció algo confusa la explicación de los modelos de recomendación, creo que debió ser más clara y y práctica.

автор: Ajay S

4 мар. 2019 г.

Good for beginner level, not for intermediate or advance level. I learned more about graphlab than anything else.

автор: Serban C S

11 февр. 2018 г.

Using a proprietary library for a paid course is not really a big issue but some people will be turned off by it.

автор: Pēteris K

23 сент. 2017 г.

Definitely a good intro to the richness of ML, but would have preferred more rigorous assignments and evaluation.

автор: Luca

10 нояб. 2016 г.

not using scikit and assigment way too easy, not challenging, but high quality video, very easy to understand .

автор: Pubudu W

10 июля 2017 г.

Good survey course on ML techniques. Not very detailed and the exercises are too simplistic for real learning.