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Volver a Machine Learning Foundations: A Case Study Approach

Opiniones y comentarios de aprendices correspondientes a Machine Learning Foundations: A Case Study Approach por parte de Universidad de Washington

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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....

Principales reseñas


16 de oct. de 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 de ago. de 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.

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3026 - 3043 de 3,043 revisiones para Machine Learning Foundations: A Case Study Approach

por Arpit S

22 de may. de 2020

Improve the quality of quizes. Need to focus more on algorithm part.

por Pratick B

8 de ago. de 2021

I​nstallation of Sforce and turi was not shown adequately enough.

por Mohamed M

28 de sep. de 2021

import turicreate is hard to install and class based on it

por Eunyoung C

29 de ago. de 2020

This course could be better to use general python library.

por Christian C

5 de jun. de 2021

El curso es bueno pero esta completamente desactualizado

por Sunita b l

4 de jul. de 2020

Provide the good notes and video so all concept clear.

por Melissa F

2 de ago. de 2021

cannot get the tools installed to do any of the work.

por Nguyen K D

18 de jun. de 2020

Coursera Scam Auto Subcription. Free Fuckers

por Gencho Z

3 de jul. de 2022

Wors ML course I've had on Coursera so far.

por Jeni

17 de abr. de 2020

Instructional videos were unclear.

por MD D I

26 de jun. de 2020

I want to un enroll this course


18 de jun. de 2020

Not a good course to study

por jazz p

23 de jul. de 2022

Poor version support

por Jorge L G A

23 de sep. de 2020

no esta en español

por fuzhi z

8 de dic. de 2020

Not recommend

por Jijo J

25 de abr. de 2021


por Bhavya C

18 de mar. de 2021



24 de may. de 2020