Chevron Left
Вернуться к Applied Machine Learning in Python

Отзывы учащихся о курсе Applied Machine Learning in Python от партнера Мичиганский университет

4.6
звезд
Оценки: 6,556
Рецензии: 1,172

О курсе

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....

Лучшие рецензии

OA

Sep 09, 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

FL

Oct 14, 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!!

Фильтр по:

851–875 из 1,156 отзывов о курсе Applied Machine Learning in Python

автор: Xia l

May 16, 2019

GREAT

автор: xuhp

Nov 13, 2018

great

автор: shubham

Mar 17, 2018

great

автор: �SADHARAN G

Aug 10, 2020

good

автор: RAGHUVEER S D

Jul 25, 2020

good

автор: N. S

Jul 07, 2020

good

автор: Arif S

Jul 01, 2020

good

автор: parmar p

May 18, 2020

nice

автор: Miriam Y R L

Dec 27, 2019

good

автор: Light0617

May 13, 2019

nice

автор: Shishir N

Jan 09, 2019

N

i

c

e

автор: Jimut B P

Oct 08, 2018

Nice

автор: Yi-Yang L

Jul 03, 2017

Nice

автор: SURAJ K

Jun 23, 2020

osm

автор: Shilpi G

Jun 02, 2019

...

автор: Magdiel B d N A

May 10, 2019

ok

автор: PREDEEP K

Nov 24, 2018

ok

автор: Andrew G

May 16, 2019

T

автор: Junaid L S

May 14, 2019

G

автор: Thomas W

Mar 06, 2018

g

автор: Oleh Z

Feb 27, 2018

G

автор: Piotr B

Jun 01, 2017

a

автор: Martín F J M

Sep 21, 2020

Course is excellent in content. Not heavy in mathematics (altough, I would recommend reading how models are supposed to work), the objectiv eis to have a practical understanding of how machine learning is applied and the important concepts to consider for a succesful model building. The focus is to have hand-on experience with the sklearn library.

I don't grant 5 starts (I hesitated for 4), as the course was designed back in 2018, therefore, you sometimes struggle with legacy libraries. Another issue, is that there are some hiccups when it comes to assignment uploads (for instance, the address of csv files!). As a student, this will make you hesistate and question wether the instructor screwed up with the autograder or not, which IS stressful.

Quiz 4 suddenly became non-forgiving, multiple choice answer have to be answered with 100% certainity to score full point. Quite anti-climatic, considering that previous quizes didn't work like that.

Final assignment is quite challenging, and might make the new student suffer.

I appreciate the instructors and Kevyn Collins for this great course. Now that I have a better picture, I get insights on how to focus my research efforts in sensor research and development.

автор: Jun-Hoe L

Jun 03, 2020

My actual rating is 3.5 stars. This is the best course yet in this Specialization.

Pros: I prefer Professor Collin-Thompson's delivery compared to Professor Brook in the previous modules. I think he gives a good overview and sufficient depth for an applied course, compared to Professor Brooks which I find to be quite superficial most of the time, and weirdly detailed in other parts. Assignment is good enough for reinforcement learning and definitely better planned. I also appreciate the link to additional readings which are quite informative.

Cons: Assignment auto-grader. This is still the biggest letdown of all the courses in this specialization Codes which work on your laptop or suggested elsewhere on Stackoverflow etc fails to pass the autograder, so 30-40% of the time of the assignment is spent on wrangling the code to pass the autograder.

Note: If i haven't taken a Machine Learning course by Professor Andrew Ng, this course would definitely be much harder. This course doesn't go to much into the background knowledge,and they mentioned this many times. But I appreciated the applied aspect, since this was what I was looking for.

автор: Carolyn O

Jan 19, 2020

I had no ML background, although I have the math the models are based on. The material seemed more than week's worth for a couple of weeks. The quizzes make sure you don't miss the key points you need to take away and need for the assignment. Most information or key words are in the slides, but course expects you to be independent enough (intermediate) to learn closely related ideas on your own via StackOverFlow and discussion forums. The discussion forums were especially helpful for this course, but then online discussions makes it more studying alone. Discussions helped me trouble-shoot and get better ideas how to approach the problems generally. I can explore and use ML and sklearn on my own, which thankfully seems to be a goal of this professor. No material could be left out, but when more videos, better longer time estimate for the week would be nice.