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Вернуться к Applied Machine Learning in Python

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

4.6
звезд
Оценки: 7,062
Рецензии: 1,287

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

AS
26 нояб. 2020 г.

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
13 окт. 2017 г.

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!!

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1201–1225 из 1,265 отзывов о курсе Applied Machine Learning in Python

автор: Robert S

1 нояб. 2020 г.

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.

автор: Mario P

8 дек. 2019 г.

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.

автор: Vatsal K

24 мая 2020 г.

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.

автор: MD T R J

12 апр. 2020 г.

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.

автор: Jun L

7 нояб. 2019 г.

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.

автор: Ishan D

20 сент. 2020 г.

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.

автор: devansh v

17 июля 2020 г.

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.

автор: Alexey F

5 мая 2020 г.

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.

автор: Oscar F R P

17 авг. 2020 г.

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.

автор: Mohamed L M

18 сент. 2020 г.

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.

автор: Sakina F

27 мар. 2018 г.

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.

автор: Marcos B

12 сент. 2020 г.

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.

автор: vikram m

26 авг. 2019 г.

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

автор: Claire Z

20 июля 2019 г.

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.

автор: Raymond C

27 янв. 2019 г.

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.

автор: Suhas A B

31 дек. 2020 г.

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

автор: Tracy S

31 июля 2017 г.

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...

автор: Sukesh K

14 июня 2020 г.

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.

автор: Jan

7 авг. 2017 г.

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.

автор: Fernanda T

4 авг. 2020 г.

Good content and I learned a lot. However, the instructor made too many mistakes during the lectures and the assignments also have mistakes that need to be fixed by the students.

автор: Ketan L

4 июня 2018 г.

Follow the course with introduction to ML with python to have descent understanding. Instructor won't be able to keep one interested for long. Exercises could have been tougher.

автор: Victor E

16 авг. 2017 г.

Two point: 1) you can learn a lot here, 2) imagine you are shown a hammer but never explained how to hit a nail. Two previous courses in the specialization do both.

автор: Kareem H

3 мар. 2020 г.

Course instrutor and materials are needed to be improved as they are very poor

Assigments\Quizes are very good and they are the mainly root cause for this rating

автор: Thomas B

7 июля 2018 г.

Some very good practical advice like dummy testing or data leakage issues Some trivialities and repetitions. Python code could have been a bit better commented