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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
10,922 個評分
2,618 條評論

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....

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BL

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

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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251 - 机器学习基础:案例研究 的 275 個評論(共 2,543 個)

創建者 Abhijit P

Jul 17, 2017

Excellent course to get you started with machine learning. Both the presenters bring a in lot of their expertise and experience to make this course fun and engaging. Lot of examples are shared which help to understand the topics in a much better way. This training is really top notch. I

創建者 Khandaker S M

Jun 11, 2020

Introduces you to this really powerful python library 'graphlab'. As a beginner, sometimes I found it hard to solve the programming problems. Also there should've been a video on how to install graphlab. I think this was the hardest part :v A really good machine learning course overall

創建者 Wan S L

Jun 12, 2016

One of the best courses I have learnt from Coursera. I love the way the lectures are presented, and how great our teachers guide us through it! I love the materials given, the projects and assignments, and the fun part interacting with teacher. Overall, five stars! Highly recommented!

創建者 Moez B

Jan 03, 2017

Excellent overview of machine learning and a great approach (case-study) to introducing the field. The instructor were fun and did an excellent job. Looking forward to the rest of the specialization for a deeper work with the math and algorithms behind the various techniques covered.

創建者 Freddie S

Jul 25, 2016

The instructors have taken great care to make "machine learning" human "learnable"! The concise unified ML framework, intuitive real-world case studies, and introductory coding exercises provide students with a very helpful "roadmap" for understanding and internalizing the material.

創建者 Carlos D M

Dec 21, 2015

Well-paced. Assignments are well executed. They are challenging enough to really force me to learn but not too easy that I'm feeling like I'm wasting my time.

This class in particular was somewhat high-level and left me very interested in the rest of the classes in the specialization.

創建者 张凌祺

Sep 29, 2015

A great course for beginners. Unlock the power of machine learning in a quite non - mathematical way. But even for people who are familiar with machine learning, you can learn some good pratical programming toolbox to help you prototyping or develop your machine learning application.

創建者 Daniel S

Dec 12, 2015

I think this course is an excellent introductory survey of the topics and technologies relevant to machine learning. The teaching method is much more than a mere regurgitation of facts and contributes to an environment where topics can be truly learned and applied in the real world.

創建者 Satish M G

Dec 09, 2016

This is an excellent course provided by the creators of this course. My sincere appreciation to both of them. The length of theory and practicals are very appropriate. I am very sure to continue all courses and finish them and master them. Thanks coursera for providing this course.

創建者 Saravana P

Dec 20, 2015

This course gave an introduction to ML concepts and applications. This course is good for absolute starters, as it doesn't scare the learner with hard core theoretical concepts. I learnt a fair bit about the overall ML scenario. Thanks to the instructors for making it fun to learn.

創建者 Saravanan C

May 26, 2017

The strategy of making people to become comfortable by first providing simple hands-on and building confidence is good. That will motivate them to stay along ALL the 6 course without getting perplexed. I see good team work! Thanks - I will strive to complete all the six modules.

創建者 Ashar M

Oct 23, 2016

Great course, focused on practical learning and some of the widely used applications, such as sentiment analysis, product recommendations, image recognition etc. The videos are crisp and to the point and you will appreciate the amount of knowledge they pack in a very short time.

創建者 Genyu Z

Jan 20, 2019

This course is very useful. Firstly, it helps me to build a perfect python environment. Secondly, it teaches me how to use jupyter notebook correctly. Teachers are very kind, and I like their teaching ways. If I can build algorithm without graphlab, it will be more challenging.

創建者 Alberto V H

Jan 14, 2017

A very good introduction to foundations of machine learning. The learning methodology based on study cases is amazing and gripping and the ipython notebooks used in the practical sessions are very instructive. Strongly recommendable for everybody who want to start in this world

創建者 Andre J

Mar 18, 2016

These Machine Learning classes have been fantastic so far, really enjoying them. Very good coverage of topics and challenging exercises to drive home the learning. The effort put into developing the classes has been superb and I look forward to the rest of the specialization.

創建者 Haritz P

Mar 01, 2016

I really enjoyed this course. It's a very good introduction to Machine Learning. I already know a little bit of machine learning, R and Weka and I liked this course. I learned Machine Learning in Python and a little bit of NLP. I'm very excited to complete the specialization!!

創建者 Rohit G

Dec 11, 2015

I really loved the course ! It helped me greatly to gain an overall idea of the aspects of machine learning at the outset itself without confusing me with intricate details of the course and yet introducing to everything in there at a glance !

Really keeps you hungry for more !

創建者 Maria Z

Oct 16, 2017

Great course, thank you very much! I had no previous experience with ML or programming, that was quite challenging for me to pass the assignment, but it was possible The material is being taught by the tutors very clear. I'm sure to continue my education with further courses.

創建者 Zachary C

Apr 30, 2017

A great primer on the various high level concepts in machine learning and some general applications as well as good quick intro to graphlab create. I was originally apprehensive to use another data science tool outside of panadas, but now think graphlab create is even better.

創建者 Miguel A P L

Nov 28, 2016

Before taking this course I would not consider the topic as something that one could learn by himself.

The Course has opened my mind and has showed me that there is a lot to learn and study in order to fully master ML and AI in order to use it in the applications we can build.

創建者 Gurunath M K

Sep 30, 2019

It was truly informative course. At the end of the course, I am sure I can say I know the all the key concepts behind Machine Learning. In near future, my focus will be be try to implement in relevant use-cases around. Thanks Accenture LKM and Coursera for facilitating this.

創建者 Supriya N P K

Dec 09, 2015

Its a very basic course and a good start to learn Machine Learning.

Course was pretty easy to follow and the real world examples helped to visualize the applications of Machine Learning. Its highly recommended for the students who are completely new to the Machine Learning.

創建者 Puppala A S

Apr 28, 2020

The course was awesome and i am willing to learn all the courses present in this specialization.Both the tutors are great and their explanation was incredible . Actually this course period is of 6 weeks but I completed the whole course within a week because of the tutors .

創建者 Peter G

Feb 26, 2016

Very nice brief introduction into the field. Gives good overview of main concepts: 1) statements of problems in machine learning 2) approaches to finding solutions 3) methods to evaluate resulting solution . Systematic material presentation with good examples and analogies.

創建者 Zachary N

Dec 13, 2015

Great overview of machine learning techniques and practices at a high level! There is sufficient material here to go from no machine learning knowledge (and a general programming background) to being able to create and deploy machine learning models for use in applications.