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返回到 机器学习基础:案例研究

學生對 华盛顿大学 提供的 机器学习基础:案例研究 的評價和反饋

4.6
9,055 個評分
2,163 個審閱

課程概述

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....

熱門審閱

PM

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.

SZ

Dec 20, 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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51 - 机器学习基础:案例研究 的 75 個評論(共 2,081 個)

創建者 Christopher W

Oct 15, 2015

The fact that the class uses GraphLab instead of pandas/numpy/sklearn should have been stated up front

The course felt like an advertisement for the professor's toolkit

It was very disappointing that the equivalent standard workflow was not supported

創建者 Ken C

Feb 04, 2017

Not happy about course 5 & 6 got cancelled.

創建者 Shane C

Nov 16, 2016

I did not find the course very good. I came into the class with only real basic knowledge of Python and I was hoping to be able to pick up more as part of this class. WHile I might have picked up more, it was only because I used resources outside the course.

The video instructions in programming in Python left quite a few gaps to figure out by reading documentation. The videos themselves were divided into two sections -- first a theoretical or classroom like section and a second a lab/programming section going over some coding in Python. The 'classroom' type lectures were pretty reasonably good. But the lab/programming were pretty terrible.

They instructor really break down the syntax of the code and just left the student to figure it out. This made being able to take this code and to adapt it to other uses very difficult.

I would not recommend this as a course to help learn Python.

創建者 Shalini G

Dec 04, 2018

Nice course to understand the basics of Machine Learning

創建者 Zohaib M

Dec 05, 2018

very good and excellent course.

創建者 Xue

Dec 02, 2018

Great course!

創建者 AMAN M

Dec 18, 2018

I was totally new to the machine learning, but this course helped me to understand what is it? What is the importance of it ? where it can be used and what will be the future of it ? There was also enough exercise work to check our understanding to the topic learnt. I think it will be more interesting if they provide a console for code snippet for the assignment... It was very nice experience with Carlos Guestrin Sir and Emily Fox Ma'am

創建者 Muhammad A N

Dec 06, 2018

Excellent

創建者 Christopher M

Dec 07, 2018

This was a great course. The instructors were fun and knowledgeable and the assignments were well-written. I loved the flexibility of being allowed to use whatever software I wanted to solve the ML assignments since the quizzes were based on the results of the modeling rather than submitted code. For some assignments I used sklearn and for others I used the software recommended by the instructors (graphlab).

創建者 Md R A

Dec 11, 2018

Great Experience

創建者 Jungshen K

Dec 12, 2018

Very comprehensive and hand-on fashioned course, recommended!

創建者 Divyansh S

Dec 25, 2018

I found this course advantageous for me. I found the case study approach of teaching the various concepts of Machine Learning quite helpful. Case Study approach gives us the idea of practical implementaton of these concepts in real life. The quality of the teaching content was very good. Moreover the assignments helped a lot in understanding some of the key concepts. Ideal course for newbies to start learning Machine Learning.

創建者 Manu S

Jan 02, 2019

Excellent course. Explained all the ML concepts in detailed and easy way.

創建者 Lukasz W

Jan 01, 2019

Very good as an introduction to the further learning of ML

創建者 Ezra S

Jan 01, 2019

The only way these courses could be better if there were far more of them from the same professors. If more of the nitty gritty details of these algorithms were fleshed out in all their glory, more algorithms, more mathematical derivations & more tutorials in the programming languages & libraries used. Otherwise, these MOOCs are near perfection. A very, very nice introduction for beginners with just a little bit of math & not too much programming. Just enough for busy people. I've reserved that 5th star due to the slow pace that the MOOCs have been released (which will presumably be irrelevant for future machine learners) & the fact that there really needs to be more of these very high quality moocs. So there aren't enough of them, so I reserve a star. Hopefully in the future that will be irrelevant as well in which case I'll regret not indicating 5 stars.

創建者 Lokesh K

Jan 27, 2019

I appreciate the effort you kept for this online course.Actually I enjoyed learning here.But you can be little bit more detailed in the ipython notebook code explanation. Otherwise ,this is the best course .

創建者 DIVYANSH S

Feb 03, 2019

EXCELLENT

創建者 Aleksander S

Feb 01, 2019

This is a great course. The content is delivered at a very good pace even for people with little prior knowledge of statistics or computer science — not too fast (would be too difficult) and not too slow (could become boring). Additionally, the assignment model is perfect — it requires completing hands-on exercises, but then the solution is assessed using simple quizzes. Thanks to that the answers and the grades are immediately available.

創建者 Walt M

Feb 04, 2019

create videos and hands on practices

Neural network part should be enhanced with more common frameworks, such as TensorFlow/Keras

創建者 Pavan B

Nov 20, 2018

What an amazing way to start the course. After first module, we know a little bit about every specialization topic. Great material.

創建者 Ankita M

Nov 20, 2018

great content and good exercises

創建者 Satish K D

Nov 25, 2018

Very informative in basics of Machine Learning. It sets the stage for a deep dive into the topics of machine learning like Regression, Classification, Clustering etc.

創建者 Kripakaran R

Nov 10, 2018

Some of the informations are half baked, hope to see them in future classes.

創建者 Myoungsu C

Nov 26, 2018

It was a great introduction!

創建者 Sanjiban B

Nov 27, 2018

Great course. Thank you.