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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
7,522 个评分
1,372 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

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.

FL
Oct 13, 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!

筛选依据:

1276 - Applied Machine Learning in Python 的 1300 个评论(共 1,355 个)

创建者 Sourav P

Oct 27, 2018

Nothing wrong really. Should have provided more mathematical theory in the resources section.

Assignments should be a lot tougher and on real life data sets which require recodings and transformations. Quizzes should be more relevant to the lessons taught. More hardcore theoretical resources, like books and research papers should be included in order to complement the practical lessons.

创建者 Muhammad H R

Jan 19, 2018

This course was too theoretical and lacked any practical exercises that would help me solve any problems. The professor went too deep into the concept and in the end you were left wondering what is the purpose of the algorithm. Seems as if they were concerned in covering a specific amount of topics rather than making the concept of machine learning more approachable.

创建者 Fatemeh M

Nov 25, 2018

Hi

First I want to thank all the instructors and anybody that was involved in this course preparation. That was a great opportunity and I really liked that but not in all parts . I think the syllabus was a little heavy and somehow I couldn't follow that . in the programming part I needed more guide and sample .

But in general It was good and I thank you so much.

创建者 Bhavesh B

Jul 7, 2021

The course was great. The only drawback was that the faculty did not feel confident to give video lectures. The videos were crisp, to the point and explained all the concepts beautifully. My only suggestion to the faculty is to record the audio clearly, as sometimes things were not clear and it became necessary to go back and watch the videos again.

创建者 Max W

Apr 24, 2021

Excellent but basic explanation of algorithms. The sample code is useful. If I did not refer to outside resources I am fairly sure I would not have been able to complete this course. The autograder really needs some work. Overall, though, I learned something about these algorithms and appreciate the effort put into preparing this course.

创建者 Robert S

Nov 1, 2020

The subject matter is interesting, but there are many issues with the assignments that should have been fixed before the course is offered, for example, unworkable code segments that remain in the assignments or that prevent the grader from functioning properly. Be sure to read the forum carefully before beginning coding assignments.

创建者 Mario P

Dec 8, 2019

I struggled with this course. The lectures cover a great deal of information extremely fast. I appreciate that there are more lectures than in previous courses in the specialization and the information is better presented IMHO. The assignments were quite difficult and I struggled. Relying heavily on discussion forums and online posts.

创建者 Vatsal K

May 24, 2020

I think the instructor must give more practical explanation for scikit-learn. I need to research almost everything for completing a particular assignment. Please have changes in pitch of your voice while delivering the lectures so the lectures don't seem boring. Also, please update the autograder !

Overall a good course. Thank you.

创建者 MD T R J

Apr 12, 2020

The course material is good, but the teaching style is too boring. Without the standstill slides, if there is animation, it would be helpful for us. And, the assignments are not straight-forward and the autograder is buggy. As an example, I can run the assignments easily in the jupyter, but the autograder faces problems.

创建者 Jun L

Nov 7, 2019

There are too many errors in the video and even in the quizzes and assignments which will affect the final grade and wastes studying time to figure out it is an error. It is pointed out in the discussion forums but no one is taking the action to correct it. Moreover, at least 3 of the reading materials fail to be loaded.

创建者 Ishan D

Sep 20, 2020

Good course for beginners. However, things like feature selection, dealing with null values, model selection should be in depth and an end to end example on a real world dataset should be explained step by step to with best practices to develop learner's interest towards picking up problems and solving on their own.

创建者 devansh v

Jul 17, 2020

Course is good but leaves a lot of things unexplained and feels like the weeks explaining ml algorithms are in a rush.But the assignments are truly remarkable.I would recommend this course to anyone who already knows machine learning and would want to apply it on some good problems/assignments before Kaggle.

创建者 Alexey F

May 5, 2020

I really like the main idea of this course, i.e., using sklearn lib along with basic lectures on the ML topic. So, I was expecting that we will be following the contents of text book by A.C. Müller & S. Guido. In the first two weeks it was really good. The materials of last two weeks were quite compressed.

创建者 Oscar F R P

Aug 17, 2020

Its a really complex topic an though videos seem long enough to explain some ascpetcs of it, many little things go under the radar and make it difficult to understand some thing. Algo, the lectures are a bit weird since the professor sometimes stutter or changes ideas mid sentence.

创建者 Mohamed L M

Sep 18, 2020

Good explanations on videos, The only problem which was really time consuming and wasting was the problems related with the assignments submission. but overall this course helped me a lot to structure machine learning fundamentals in my mind and to get a good practice out of it.

创建者 Sakina F

Mar 27, 2018

The videos are way too long and very monotonous. They should be cut down and reduced. The maximum length they should be is 5-6 mins other wise they becoming distracting.

The course content is good though. Quite easy to understand but going through the videos is a chore.

创建者 Marcos B

Sep 12, 2020

I think that the subjects are very advanced. There should be a more clear specifications of prerequisites for the course. I had to look for lot of help outside the materials provided for doing the activities. The course is fine if you have the apropiate skils though.

创建者 vikram m

Aug 26, 2019

It's a good course, but a quick one. One needs to have a beforehand knowledge of all the algorithms as they are not discussed in details. State of the art is not mentioned. Implementation and best practices are present, along with pros and cons of each algorithm

创建者 Claire Z

Jul 20, 2019

The course is quite high-level. There is nothing wrong with an applied course being high-level. The material is easy to follow, the quiz is a bit challenging but the homework assignments are quite easy to pass. I prefer a course with more fundamental details.

创建者 Raymond C

Jan 27, 2019

The course is too tight, just 4 weeks cannot master the machine learning. This course can splitted into 2, in order to capture more on the deep learning and unsupervised learning, which are important, but being categorized as option in the course.

创建者 Suhas A B

Dec 31, 2020

Good content but too fast paced for someone without even the slightest basics on ML. The first 2 courses in the specialization did not prepare for this course. To make full use of the course get ML basics right and then maybe come here

创建者 Tracy S

Jul 31, 2017

the second assignment was a little beyond what was taught in the lecture. others are fine.

big suggestion: please please have a better auto-grader. Most of my time was spending on how to battle the auto-grader instead of coding...

创建者 Sukesh K

Jun 14, 2020

Course is well structured, course material also is well defined and learning is excellent. Though Instructor's communication is very laidback. Should have more engagement in tone and connect with enthusiasm.

创建者 Jan

Aug 7, 2017

Quick tutorial-like overview. Autograder is not too verbose and as a result I spent some time struggling with debugging the code rather than figuring out how to solve machine learning related problems.