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Learner Reviews & Feedback for Supervised Machine Learning: Regression and Classification by DeepLearning.AI

4.9
stars
23,866 ratings

About the Course

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start....

Top reviews

FA

May 24, 2023

The course was extremely beginner friendly and easy to follow, loved the curriculum, learned a lot about various ML algorithms like linear, and logistic regression, and was a great overall experience.

AD

Nov 23, 2022

Amazingly delivered course! Very impressed. The concepts are communicated very clearly and concisely, making the course content very accessible to those without a maths or computer science background.

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4626 - 4650 of 4,718 Reviews for Supervised Machine Learning: Regression and Classification

By bhargavi g

•

Aug 30, 2023

good

By Kshitiz B

•

Jul 19, 2023

none

By Rohan M

•

Jul 1, 2023

Nice

By Rahul B

•

Mar 27, 2023

good

By Aman K

•

Dec 21, 2022

good

By KARNATAKAPU V D

•

Nov 16, 2022

good

By Anmol K

•

Jul 23, 2022

good

By BAMBA A

•

Feb 3, 2023

RAS

By Prajwal M

•

Aug 22, 2024

-

By Mohit G S 2

•

Sep 25, 2023

Title: Frustrating Experience Due to Speaker and Technical Issues I enrolled in the course "Supervised Machine Learning: Regression and Classification" with high hopes, but I was left thoroughly disappointed. The most frustrating aspect of this course was the speaker's voice. At times, it was so annoying that it made it nearly impossible to concentrate on the content being presented. The speaker's tone and delivery lacked enthusiasm and clarity, making it difficult to stay engaged. To make matters worse, there were frequent microphone issues throughout the course. It felt like the words were either muffled or slurred, which not only made it hard to understand but also incredibly irritating to listen to. These technical problems seriously hindered my ability to learn and absorb the material effectively. While the course content itself was decent, the combination of the speaker's annoying voice and microphone problems made it a struggle to complete. I expected a much more professional and engaging learning experience, and sadly, this course fell far short of my expectations. Title: Valuable Content, Could Use Some Improvements I recently completed the course "Supervised Machine Learning: Regression and Classification," and I have mixed feelings about my overall experience. On the positive side, the course content was comprehensive and provided a solid foundation in supervised machine learning. The topics were well-structured, and I appreciated the depth of coverage on regression and classification techniques. The examples and exercises were helpful in reinforcing the concepts, and I did gain valuable insights into these subjects. However, there were some notable drawbacks. First and foremost, the speaker's voice occasionally made it challenging to stay engaged. There were moments when the tone and delivery were less than engaging, which detracted from the learning experience. Additionally, there were technical issues with the microphone that affected the audio quality. This made it difficult to understand certain parts of the course and was quite bothersome. In summary, the course content itself is valuable for anyone looking to learn about supervised machine learning, particularly regression and classification techniques. However, improvements in the speaker's delivery and addressing the technical issues with the microphone would greatly enhance the overall learning experience.

By e t

•

May 7, 2023

I'm a 45 year old software developer that never chose to progress in math beyond college algebra. I never needed it. when i took college algebra in highschool, most of the calculus related notation was not covered; this may have changed since the early 90s, but it isn't realistic to know where someone is sitting in regards to calculus. This course presented itself as if you would not need to know calc to keep up with the logic involved, but you really do need to have a good grasp on the notation involved. The frustrating thing for me is that I had to spend more mental energy just trying to digest the logic, and it really took away from the much more important content of knowing how and when to use the algorithms covered here. The math is already encapsulated in machine learning libraries, so the heavy work of trying to keep up was really mostly wasted. I think the course needs to be more up front about the math related background needed to step into it, or it should be taught with much more emphasis on applying the concepts involved in how and when to use the algorithms to create useful models. With more honesty about the background requirements to keep up, or with a shift in focus towards application, i would be giving this course a better review. but 3/5 or 3.5/5 is the best i can offer.

By KurwaFellow.in4k

•

Oct 21, 2023

I enjoyed the way fundamentals were thought and it was comprehensive yet simple. I only have few complaints: 1. the practice lab and quizzes could be a bit harder and more twisted. and the number of programming assignments could be more 2. it will be great if the course focuses a little bit mode on building models using libraries such as scikit-learn rather than just explaining the theories and mathematics behind it. 3. it will be helpful to ass subjects such as cross validation, XGBoost, pipelines etc. that are essential to build an accurate model to make learners more familiar with the real word problems of training a model rather than just explaining the basics thanks for such a good content again.

By Yogev H

•

Sep 9, 2022

I don't know if I'm perhaps not the target group of this course, so perhaps my review isn't relevant.

The course itself felt very slow and shallow, and did not give me a lot of theoretical background that I was interested in understanding.

Additionally, I find the optional labs, quite cumbersome to load and play with, additionally the practice quizzes were sometimes phrased a bit confusingly.

However, I do have to say that I understood the main ideas. It's just very not challenging.

By Javier B

•

Jul 3, 2023

The course provides a lot of very good high level information in Machine Learning (Regression and Classification) , but it feels like I was strolling through the park. The course lacks rigor, but makes up for it with very good explanations and intuition. Still leaves you wanting more. I think you are going to take up a students time, provide them with the same level of information as a graduate student. After all, they want to use this in the real world, do they not?

By Andrew L

•

Jun 30, 2023

Not as good compared to the previous machine learning course (the one done with Octave), you aren't pushed to write the vectorised implementations, even the example code and starter code in assignments is done with loops. No introduction into the "@" syntax of numpy. I prefered the math syntax of the prev course with hypothesis function and Theta for weights, including adding the bias term itself as theta_0 within the weights vectors.

By Daniel S

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Sep 19, 2023

The educational material was good, but the assessments were very simple. The quizzes and assignments could have been more thorough to properly assess whether I had grasped the material, rather than "fill in the blanks" and multiple choice (though often only 2 choices) style problems. A certificate from this course is not a good indicator that the student has learned the material.

By daniel c

•

Sep 21, 2024

The course dives too deep into the math behind the type of regressions to make predictions. But it lacks practice in using python libraries to actually put them into use. Instead of having to calculate each operation manually it should present the learner with more opportunities to implement python machine learning libraries to get some hands on experience.

By Praveen K T

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Jun 17, 2023

Error that was shown after an assignment is done was not helpful to debug on where the error is and took couple of hit and trial to work. The cell blocks said all tests successful but was unable to submit the assignment. User friendly text explanations on where the error is, Highlighting in red etc could have been helpful.

By David M

•

Apr 29, 2023

The videos were great and it was an excellent introduction to machine learning. However, I feel like the quizzes should test our knowledge a little more. The questions are too simple and makes it so that I am not sure if I actually have a good understanding of the material being taught.

By Waleed S

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Oct 2, 2022

Practice labs are not well organized as previous course. The optional labs have less hands on activities which gives less hands on activities for the learner. It would be great if there could be more activities that the learner could perform instead of just looking at the code

By Yevhen O

•

Sep 5, 2023

It's okay for intro but I feel need in more practice. Don't expect to get skills from this course. You will get a lot of new theoretical information with just a scratch of practice. So I suggest to mix this course with some good books + practice.

By Parsa G

•

Mar 2, 2024

it was a really great theorical course but the practical parts were too small. so I know alot about the concepts and I can calculate stuff but i can not implement them that much , and I'd rather the course to have more difiicult math! well done!

By Prathmesh

•

Sep 9, 2024

Could give a bit more focus on the coding part. What I mean is rather than giving notebooks with already written code, you could teach the code to the students and have them write it themselves.

By Jorge M

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Feb 28, 2023

It is really interesting, I think I have miss more focus on connecting everything together from the function f_wb to cost function to deltas to preditions. The labs are not very challenging.

By J.P B

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Mar 29, 2024

Code cannot be transferred easily to jupyter notebooks or google colab. The course was 90% theory. Needs more real-world practice and projects. Other than that Theory was very good.