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學生對 IBM 提供的 使用 Python 进行机器学习 的評價和反饋

4.7
5,648 個評分
735 條評論

課程概述

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course....

熱門審閱

RC

Feb 07, 2019

The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.

SK

Aug 04, 2019

The instructor was awesome. His voice was crisp and to the point. The course is actually well laid out with proper structure. Altogether a great learning experience. Cheers... Keep up the good work.

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26 - 使用 Python 进行机器学习 的 50 個評論(共 734 個)

創建者 Erik C

Jul 04, 2019

This was a good course to see how the basic ML models can be used with clear examples in Python. It was a very good sequel to the Stanford as this course didn't go into detail on the algorithms or any depth in to the math behind the scenes. In fact, you could ignore the equations and still do fine. Unfortunately, I didn't feel I learned enough, specifically about how to tune the parameters and improve the results of different algorithms. The final could be accomplished by simply cutting and pasting the work done in the non-graded 'labs' and providing any level of accuracy scores. I would have welcomed more depth on optimization. Also the hardest part of the course was using matplotlib but you didn't even need to understand it to pass. Overall, I'm glad I took this course. It was very helpful in my learning journey.

創建者 David D T

Jul 08, 2019

The Machine Learning with Python course was very challenging. The final assignment, though, seemed to require knowledge not yet learned, which made it rough to complete. Also, although I completed the notebook, all of my cells were not visible to the reviewer even though my settings were such that all cells should have been visible to him/her. I restarted the kernels and re-ran my code a couple times and it was finally visible when I opened the shareable link. That delayed my receipt of an accurate score for a few days. Ugh.

創建者 Sisir K

Apr 03, 2019

Very complicated subject. Many lines of code in the algorithms are not explained, and the learner is left to either figure out their function themselves or to memorize them.

The final assignment was fun to complete.

創建者 Parth R J

Mar 03, 2019

very bad course

no proper instructions or explanations in videos

創建者 Serdar M

Dec 10, 2018

labs are not easy to understand

創建者 Sean E B

Oct 16, 2019

It's really not very good. It's extremely frustrating and poorly made. It barely helps equip you with any practical python machine learning knowledge.

Good points:

Videos provide a good overview of the overall concepts and ideas

The videos and quizes are logically set out

Bad points:

The practice notebooks contain a lot of code and information which is not explained and didn't really come up on the videos. Often, you do not know why the code is there, how to make it, or what it does. So you hardly get any practice using code for machine learning.

The final project is a joke. The instructions are not clear, insufficient, confusing, and contains grammatical/spelling mistakes. For example, the instructions for the final project as you to find values that are impossible for the type of model you're making. The course makers obviously just copied and pasted stuff and didn't check it.

To make it worse, people have pointed out the mistakes and errors in the forums, but the course makers are either too lazy or don't care enough to fix it.

The forum is full of people asking for help and there is barely any clarification from the staff.

The course could be improved so much by having clearer and more instructions/annotations. However, it seems like IBM is satisfied ignoring the glaring problems present in the course.

You money and time would be much better spend on another machine learning course. But if you're like me and have done the other (comparatively better) IBM data analysis courses i guess you have no choice but to do this one in order to get the final certificate.

創建者 Siavash A

Nov 06, 2019

This course must be taken off from Coursera. Here is just a couple of reasons: There is very confusing typo and bad description of the final assignment, even though it has been reported, they didn't take 2 minutes to fix it. The final assignment is peer graded (which is really stupid, how do other clueless students know if I did it correctly or not) they provide an answer sheet (not the solution) and I think a lot of students thought it was the solution and marked my assignment incorrectly. The quality of the content provided is poor... If someone knows Python already, then they're wasting their time really, if they don't - this course does not teach them anything... Don't waste your time and money - there are much better options out there.

創建者 Chang C

Dec 08, 2019

I am very frustrated with the course's final project. Please, when you ask for tuning meta-parameters, either be specific or do not provide a false out-dated solution where there is no tuning at all in decision tree, svm, nor regularized logistic regression. Not every new-to-stats understands your misleading instruction of the final project or can be capable of grading according to what is actually correct.

The instructor should be more aware of this issue. I ask for a refund, it doesn't worth my money!

創建者 Girish O

Mar 20, 2019

Very confusing and very limited details. I am not sure I understood anything. It is not explained very well at all. All the topics were just read by the narrator/author/professor. I will not recommend this course to Non-math background people like me. Extremely difficult to understand any concepts mentioned in this entire Course.

創建者 Michael T E

Sep 19, 2019

The response from the teaching staff was barley there. Lab work laid out what the 'target' (beginner-intermediate ) users should know to complete lessons. Peer graded assignments required much more than what was taught in the lessons. I spend more time researching tasks then learning them in a paid course.

創建者 Derek A

Jun 03, 2019

Was a 3 stars until final week. Stuff is explained and is written poorly. I honestly felt like I got shammed by last week. I had to look online at other YouTube videos and forums and I am just not happy with what I got out of this course. I will be doing Andrew NG's course on YouTube now..

創建者 Ubaid M W

Oct 22, 2018

In lab there are many funtion , libiraries Which have been used first time with out any description , then I have to search for each and every funtion or lib which is way time consuming which make this course worst courses in my list.

創建者 Stephen P

Mar 10, 2019

Lots to learn in this class! Week 3 was definitely heavy and challenging in the middle of it, but the course really builds up well and makes sense by the end of it and I understand why those topics were combined as they were. I found the labs most helpful when they included # hashtag explanations/documentations when introducing new code to explain the different parameters and reasons for using them, or if establishing parameters in the code with explanatory definitions/names to guide the user through new operations. In the very last lab, I think they included a link to the pandas API reference page with that specific new operation. I found that really helpful because I had already been going to the pandas page to learn more about other new operations as they were introduced in previous labs.

創建者 Kalpesh P

Nov 29, 2019

I personally felt, it is one of the best modules offered as part of certification program. Data science has large number of algorithms, so naturally it is difficult to cover most of them and more importantly it is difficult to decide where to start from. Module is well designed, and it has provided basic to intermediate knowledge of most of machine learning algorithms, must to know for beginners. Few minutes introductory video on any given algorithm, followed an hour-long lab practice is really helped to understand algorithm and it’s implementation using python. Provided structured course really helped me to perform machine learning implementation using python. Great content to spent time on!

創建者 akshay s

Aug 09, 2019

I am thoroughly enjoying the course. The codes written are the shortest possible codes but the narrations are just fabulous to comprehend and remember. I need more practice to write the codes correctly by my own but my fundas are all cleared and I know exactly why am I doing the next step. Having worked my way through the IBM Data Science courses, this one was the "pay off" - it was so cool to finally apply more sophisticated techniques to real world data sets. The labs were fantastic. Highly recommend this course to anyone interested in learning about the most popular machine learning algorithms.

創建者 Caterina F

Nov 24, 2019

Machine Learning with Python is highly informative and very well presented. It wasn't easy, it requires a good understanding of Math. Complex concepts of machine learning algorithms are explained clearly.

After the course, you will have a solid awareness of how machine learning is applied to the real world and how to use the skills like, sci-kit learn and SciPy from the Python language.

Excellent support of the labs and the Notebooks provided. The final project will be a challenge for what we have learned.

I strongly recommend this course.

創建者 Sri K P

Apr 14, 2019

This course is an excellent platform to understand the basics of Machine Learning with python. The lab tools pioneer a way to understand the code and implement it. The videos are crisp and clearly mention the scope of the course which creates a curiosity to know more. However, the peer graded assignment is not an efficient way as 'sample notebook" paves the way to plagiarism. The peer grading also restricts the user creativity to write a simpler code as it may not be understood by other peers. Overall I am very happy with the course

創建者 Christopher S

Jan 14, 2020

Excellent content and relatable use cases. As a beginner in data science with no formal programming, the information is presented in a way to help you understand the fundamentals and then apply them using the pre-built python packages that are widely available. I started with data science 3 years ago and it was very difficult to get started without any programming or statistics background. This course does a tremendous job of making it accessible, understandable and quite frankly a lot fun in the process.

創建者 Iskandar M

May 06, 2019

This course needs basic knowledge on algorithm and programming experience. I really recommend this machine learning course for those who have computer science, statistics, or math background. The instructor is very clear, concise, and using simple diction when explaining the subject. All presented in here is valuable and worth reading and listening. The final task is somewhat challenging, but we'll have to really dig into the examples presented in the labs. Thank you!

創建者 Peruru S S

Dec 11, 2019

I really enjoyed taking this course. The instructor is to the point, crystal clear. Nicely explains the essence of the topics in 5 to 6 minutes. I recommend this as a good introduction course to get a basic overview of different algorithms. However, if one wants a deeper understanding with specific details, this is not the course. This course will definitely serve as a good introduction which help us to get motivated to do more advanced courses.

創建者 Clarence E Y

Apr 22, 2019

This course will challenge learners to commit to learning about the key objectives for using algorithmic approaches to answering important business questions using data. The lectures cover the theoretical foundations of the "relationship" algorithms used for classification and clustering methods. Additionally, the labs provide a fully integrated environment in which learners can do hands-on investigations to gain proficiency.

創建者 Haroldo D Z

Sep 30, 2019

Hay un nivel de Detalle en los Algoritmos de Machine learning, que ayuda a entender como pueden aportar realmente en diferentes problemas de regresión, clasificación, clusterring y recomendación. y la plataforma es muy practica para lograr entender como un lenguaje como python puede aportar a hacer mas sencillo la aplicación y uso de estos sin necesidad de instalar herramientas ni conocer los detalles del lenguaje.

創建者 Niladri B P

Jun 22, 2019

A lot of ground is covered here. So it won't make you an expert, but will provide a great base from which one can build further expertise. The videos explain the concepts very nicely, so it is important to sit, listen and take notes. The labs are also very detailed and occasionally a bit advanced with the code. Overall, however, the course makes you work but you can choose how much work to put into it. Recommended.

創建者 William B L

Mar 27, 2019

This course gives a good introduction (theory and applied) to a variety of machine learning methodologies. The presentations are well thought-out. The labs are great. I learned an enormous amount from doing the hands-on work in Watson Studio/Jupyter notebook.

This would be a bit much for a beginner in Python, but with a modest understanding of the language, this offers a lot!

創建者 Juan R

Sep 09, 2019

This Course is awesome to learn the theory and practice of some Machine Learning Metods.

By the end I feel like I can tackle my own datasets and analyze them with various methods seeking the optimal one.

The only thing that could be better is if the course could go a bit deeper into the optimization algorithms (like gradient descent) even if it's a bit mathy.