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学生对 密歇根大学 提供的 Applied Machine Learning in Python 的评价和反馈

4.6
8,013 个评分
1,460 条评论

课程概述

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python....

热门审阅

OA

Sep 8, 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

AS

Nov 26, 2020

great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.

筛选依据:

1326 - Applied Machine Learning in Python 的 1350 个评论(共 1,454 个)

创建者 Shashi K

May 18, 2020

very good learning

创建者 hamzaoui m

Jul 25, 2019

HARD BUT GOOD

创建者 Dr. K

Oct 2, 2020

nice course

创建者 Aditya V

Jul 3, 2018

Excellent!!

创建者 ISHAN H S

Jul 23, 2017

Awesome !!!

创建者 KILLANI T

Jun 10, 2020

hard a bit

创建者 Diego M

Jun 29, 2022

Excellent

创建者 Deepak T

Jan 13, 2020

Very Good

创建者 Md J A

Aug 18, 2017

very good

创建者 MOHD A

Sep 10, 2020

perfect

创建者 NITYA B 2

Oct 17, 2021

Good

创建者 tanmoy p

Dec 18, 2020

good

创建者 sushant

Nov 28, 2020

Good

创建者 Anant k

Sep 26, 2020

GOOD

创建者 Sajal P

Aug 12, 2020

....

创建者 Latha B N

Jul 9, 2020

Good

创建者 Yzeed A

Oct 30, 2019

Good

创建者 Manas C

Dec 12, 2021

ok

创建者 Ketan S R

Jul 4, 2019

.

创建者 Shubham J

Mar 2, 2022

Here's my review for this course - The good aspects - - This course served as a good refresher for traditional ML concepts like Regression, Classification, and Model Evaluation, along with hands-on exercises in Python. - Assignments need effort, have good exercises & force you to think. You cannot simply watch the lectures & complete them straight away. - I especially liked the module about Data Leakages and how it impacts our model's performance. Scope for Improvement - - Some concepts like Classification models are explained pretty well whereas others such as Regression, and Unsupervised learning (Clustering, Anomaly Detection) are quite rushed. - There are some obvious errors in the assignments and auto-grader, missing files, some clearly vague questions. The discussion forum is riddled with similar questions for these errors - they could have fixed it years ago but chose not to. - Not much depth in the topics - beginners will have difficulty understanding pitfalls of certain models, how real-world data mining works, and how to select features and models.

If you're a beginner - it will give you a good overview of traditional ML models and implementation in Python. Good to try, but you need to spend a lot of time for self-learning the concepts, specially the mathematics behind these algorithms.

创建者 Nigel S

Jun 9, 2019

This is an OK introduction to Machine Learning. It covers a range of relevant topics. The gap between the lecture content and the assignments is the typical chasm for this U.Michigan "speciality", and frankly you end up basing assignment answers more on internet research rather than lecture content.

I'd sum it up as a substantial missed opportunity. The last assignment is really good in terms of doing a realistic Machine Learning project, but the preceding course content doesn't give you the tools or frameworks to do that project in a logical, industry standard workflow. It gives you an idea of what the tools are, but not how to really apply them all together in an efficient and logical series of steps.

It's as if those who designed the course decided that learners needed a tough-love approach, like a trainer lying down on the grass and showing learners swimming strokes, and then just throwing those learners into a pool and expecting them to keep afloat, and combine what they remember with what they see other more experienced swimmers in the pool doing. It shows a fundamental misundestanding of the Coursera learners usually being very time poor and expecting much more from the instructors.

创建者 Jonathan B

Oct 21, 2017

This course provided a good structure and order to learn introductory machine learning concepts in Python. However, I thought the lectures in particular were needlessly more abstract than the previous data science courses in this specialization.

In my experience, learning a new programming concept comes from practically writing code then observing what happened. The earlier data science courses were great because you could test code with the lecturer as the video progressed and learn from it.

The lecture content here structured to discuss broader machine learning concepts, rather than setup to follow along in the notebook. I found this was okay for introducing the idea of different machine learning concepts, though without the practical application and observation it became difficult to remember these concepts or test what I was hearing. I found most of my learning happened in the assignments or by following more practical online resources. The course could be improved by tying the notebook modules more closely to the video content, making it easier for learners to follow along.

创建者 Ryan D

Jul 15, 2019

I'm glad there was an introductory course like this offered for machine learning. The content is very accessible and the assignments are simple enough to work through without frustration, but challenging enough to help you understand how to apply machine learning algorithms on your own.

I did purchase the book recommended, Introduction to Machine Learning with Python by Andreas C. Muller and Sarah Guido. The lectures in this course are basically paraphrase the book. Frankly, I think you'd get more value from this course if you read Chapter 2 in its entirety and follow along with the juypter notebooks provide with the book. It's easy to tell when someone is teaching you vs. reading to you— this course's lectures were definitely the latter.

创建者 Jennifer W

Nov 11, 2020

I felt like each standalone topic was explained okay, but I didn't get a good big picture understanding at the end. There wasn't a good wrap up to explain holistically how to choose one classification method over another.

There are also just too many mistakes in the lecturer speaking as well as in the slide. For an online class I would expect that Coursera would redo the video or at least the slides as they interfere with learning.

I felt that the lecturer belabored easy points, like calculating precision and recall by hand but then didn't explain other topics regarding the classification methods well. I did not find the graphic visualizations in his slides helpful in explaining hyperbolic tangent functions.

创建者 Dimos G

Sep 3, 2019

This course was a complete disappointment. First of all, it should have been split into two courses. The second week especially contains so much material to the point that it's not-pedagogical. Also, I regret to say that the instructor is not fit for this task. It would be better if they used Christopher Brooks from the first two courses as he is more engaging and he seems to have a lot more experience in public talking. Another thing is that there are serious bugs with the assignments. This course needs serious redesign.

All in all, don't spend your precious time and money on this one. There are better courses available on this subject.