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

4.9
stars
17,990 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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126 - 150 of 3,770 Reviews for Supervised Machine Learning: Regression and Classification

By Jishnu D

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Jan 14, 2024

Exceptionally detailed and compact at the same time. After completing the course, I can confidently say that I am able to understand the basics of supervised learning completely, how to train a model and how to fine tune them. I had completed this course in 2017, and have started again inn 2023 to go through the course in python and know about the latest changes that have happened since the update to the course. But, this course has been made much better (it was already excellent at that time), and I cannot recommend it enough to all the people who had also completed the course before the update.

By kishan s

•

Sep 1, 2023

I recently completed the "Supervised Machine Learning: Regression and Classification" course on Coursera, and I can confidently say that it exceeded my expectations in every way. This course is an absolute gem for anyone looking to delve into the world of machine learning.

The course content is comprehensive, well-structured, and beautifully explained. From the fundamentals of regression and classification to more advanced topics, each concept is presented in a clear and concise manner. The practical examples and real-world applications make it easy to grasp even for someone new to the field.

By Theofanis S

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Aug 17, 2023

Enrolling in this course was an exceptional decision. Instructor adeptly demystified complex concepts such as linear regression and logistic regression, providing an engaging and accessible learning experience. The practical labs, enhanced by insightful visualizations, solidified my understanding of these techniques. The course's well-structured quizzes and assignments offered valuable assessments of my progress. In essence, this course is an invaluable resource for anyone seeking a comprehensive understanding of supervised machine learning, making it highly recommended

By Anjula U

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Jun 16, 2023

One of the best online courses available is a course focused on mathematical concepts. This course stands out because it offers highly valuable practice classes in addition to comprehensive content. The course is designed to be accessible and easy to understand, using simple English to explain complex mathematical ideas. It provides a strong foundation in mathematical principles and offers practical applications to enhance learning. Overall, this course is highly recommended for individuals seeking to improve their mathematical skills in an online learning environment

By Elyar Z (

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Feb 27, 2023

This course did an excellent job of explaining the fundamental concepts behind these important techniques in machine learning. I learned a lot about how these algorithms work and their practical applications in real-world problems.For anyone looking to deepen their understanding of regression and classification, I highly recommend this course. It's a great way to gain a solid foundation in these important techniques, and I feel much more confident in my ability to use them effectively in my work. Thanks to Andrew Ng for offering such a great learning experience!

By Narendra R

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Sep 4, 2023

This is such a solid foundation for ML and AI that this course should be included in high school education (at least as an AP class). For someone who has not been close to solving math problems for over two decades, Andrew's articulation of the content with ease, made me recollect (forgotten long back) concepts that I learned during my pre-university and Engineering days. Will definitely continue with related content and strongly recommend this as a basis for any aspiring AI/ML engineer or people who wants to know - how the damn thing works behind the scene!

By EHSAN H

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Jul 21, 2023

"I believe that it would be more effective to design tests and practice exercises that are challenging and complex. Just like how gold is highly valued because of its rarity and difficulty to obtain, the Certificate becomes more valuable and rewarding when we are faced with challenges that push us outside of our comfort zone. By providing more difficult tasks, we can encourage students to develop their problem-solving skills, critical thinking abilities, and resilience. Ultimately, this can help them to become better learners and more capable individuals."

By YASH G

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Jul 17, 2023

"Supervised Machine Learning: Regression and Classification" by Stanford and DeepLearning AI by Andrew Ng is an absolute game-changer! 🚀 This course seriously helped me build a solid foundation and get the hang of machine learning, all without drowning in math. It's like they turned complex concepts into bite-sized nuggets of knowledge. Plus, Andrew Ng's teaching style is on point! 🙌 If you're serious about leveling up your ML game, give this course a shot. Trust me, you won't regret it! 💯 #MachineLearning #Stanford #AndrewNg #LearningMadeEasy

By Kelli W

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May 10, 2023

This course was very challenging to me, but Andrew Ng is a great teacher. I took this course because I wanted to finish some of my own personal NLP (natural language processing) projects that have been languishing for the past couple of years. I augmented the material in this course with Speech and Language Processing text by Dan Jurafsky. The optional labs are super helpful, and I did all of them. I worked through everything, including watching the interview (at the very end) with Fei Fei Li. Very inspiring and thought provoking. Thank you!

By Justin B

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Aug 18, 2022

Starts off easy and then gets a bit more challenging. I enjoyed it. A couple feedback points:

- More questions throughout the videos might be helpful. - I'm not sure the labs should be designated optional, since the final labs expect you to write some code.

- It would be nice if there was more coverage on how to do feature engineering (ie. how do you know when to map original features to higher dimensions and orders? I feel like that might be one of the missing links to actually try to "do" machine learning on some practice datasets.

By Shahar B

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Feb 23, 2023

I thoroughly enjoyed the course and gained a wealth of knowledge from it. It is worth noting that while the free enrollment provided valuable insights, the absence of coding assignments limited my ability to fully immerse myself in the material. However, upon enrolling in the paid course program, I was pleased to find that it did include coding assignments, which greatly enhanced my learning experience. As someone who values hands-on experience, the coding component was crucial for solidifying my understanding of the subject matter.

By Kyle S

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Jun 9, 2023

on the final lab, exercises 4 and 5 were extremely confusing when they tried to add the "fill in the blank" style for you to finish the code. I was confusing because the hints were not formatted in the same way at all so it was very frustrating and actually hindered my understanding of what I was actually doing as I eventually just was throwing things at the wall until something stuck. Which is how I finished those exercises on the final lab. Other than that it was all very straightforward and is a great resource to have available.

By A.I

•

Nov 26, 2023

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. Use linear regression models to fit a straight line or a polynomial curve to a set of data points and predict the output value for a given input value. Use logistic regression models to classify data into two categories and estimate the probability of belonging to each category.

By Anuj J

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Dec 13, 2022

Outstanding beginner level course that introduces regression and classification with Python. The class is light on the math and coding, but it gives a fantastic overview of the topics, and provides excellent visualizations to build intuition. Andrew Ng also provides a lot of very useful tips for machine learning practitioners (i.e., we don't use linear regression for classification problems!). Very much recommend this course for anyone, whether you are a seasoned ML developer, or you want to just start your journey into the field.

By Rohit T

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Dec 9, 2023

I've gained so much from this course because it has improved my understanding. The way the course is structured, the hands-on assignments, and the real-world examples have given me a strong foundation in the concepts. Andrew's teaching style is straightforward and engaging. He makes complex mathematics easy to understand. I now feel confident and motivated to delve deeper into the world of machine learning. Kudos to Andrew Ng and the entire team behind this course for creating such a valuable and empowering learning experience!

By Konstantinos Z

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Jun 22, 2022

Very well structured course with great explanations in the appropriate pace. The maths are discribed clearly and the connection between algebra and algorithms (Machine Learning) becomes and easy process.

The assignments are in the indermediate level and the student should understand the theory/maths to complete them with 100% grade. They are all explained in the lectures videos but you need to think before you submit them.

Overall, is an upgrade of the previous course that is adjusted on Python and Jupyter Notebooks. 5/5 stars.

By Sam A

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Feb 25, 2023

Fantastic learning experience. A novice with little or no technical knowledge can grasp the essence of Supervised Learning pretty rapidly. Yes, the coding aspect requires a bit of focus & practice no doubt. Just this course alone, will expand your ML knowledge & confidence to solid levels. You begin to get a good feel for the jargon of AI/ML. Highly recommended for newbies, execs and folks looking to make that career shift in a systematic way. Dr Andrew Ng is pure genius with simplicity at his core. Thank you Dr Andrew Ng.

By Daniel D

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Jan 17, 2024

This course held significant value for me. While I typically appreciate all course content, what truly stood out was the exceptional communication skills of the teacher. Having been self-taught for a major part of my life and engaging in online courses, I've experienced a tendency for monotony that eventually diminishes my enthusiasm. As someone who also enjoys teaching, Andrew Ng's instructional approach reignited my excitement and instilled hope that teaching in such a dynamic manner can indeed be highly beneficial.

By Terry M

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Sep 29, 2023

Very well put together materials and instruction. I had taken Ng's earlier course on this topic ( using MATLAB/Octave ) about five years ago and I found much of the details to be rather opaque due to the highly vectorized ( albeit elegant ) code. Breaking all the components open explicitly using loops in Python gave me a more effective framework for learning in this version of the course. Ng and his crew have nailed it here - a 'goldilocks' treatment of the material - not too difficult and not too easy either.

By Harshil H V

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Jun 11, 2023

First of all thank you very much Coursera and team and very big thanks to prof. Andrew for give the opportunity. I am truly Satisfy with this course and throughout this course i learn so many things for the basic which new for me at least.. i will use my this new knowledge for better future for me along with society. Once again thank you very much team Coursera and special thanks to Prof. Andrew Ng. I will also recommend my friend my college who want to learn more and more deeply.

Thanks &Regards.

-Harshil Vasani

By Sergey M

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Jul 10, 2022

While I expected this to be simple Python refresher on the originally taken old course with MatLab/Ocatve, carefully reading into the code before executing it helped to conceptualie what I amd doing more. Also I really appreciate the interative demos, and especially those of gradient descent - they really add so much more to building your intuition -- make sure to click in the horizontal direction more anf more to the right and think why the results are changing in the way they do...

Thanks for this experience!

By Taiwo F

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Jan 18, 2023

Thank you for the opportunity to take this course through financial aid! I enjoyed the way the course is structured including the optional practice labs and the programming examinations! Being a graduate student with lots of responsibilities, the flexibility of the course allowed me complete the course at my pace without which I would not have been able to complete the course. I would like to take the remaining 2 more courses in the series to give me a proper grounding in the Machine/Deep learning. Thank you!

By Priyadarshi S

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Mar 16, 2023

This course was broken down to smaller understandable pieces beautifully. The in-video questions were great and the all the labs were very well designed to soak in all the theory components.

I come from a place where I am scared of Math. The way it is taught helped me embrace ALL the Math seamlessly!

Congratulations to the team!

From a place where I was wondering am I good enough for this learning, to completing the course, congratulations to me as well :)

Can't wait to deep dive into the next course :)

By Riccardo P

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Feb 23, 2024

A very in depth course that lets you dive in into the world of Machine Learning. I think people that already have some python knowledge will enjoy this course more. Optional labs also aren't really optional, unless you simply want to hear lectures on linear algebra and calculus. In parallel with this course I recommend studying and practicing on modules like pandas, numpy, and matplotlib - that let me enjoy way more the course itself, than what I assume it would have been without some knowledge.

By Ralph C

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May 2, 2023

This is an excellent course for beginners in machine learning. Andrew Ng, a world-renowned expert in the field, teaches it. Andrew does a great job of explaining the concepts in a clear and concise way. He also provides plenty of examples and exercises to help you solidify your understanding of the material.

I highly recommend this course to anyone who is interested in learning about machine learning. It is an excellent introduction to the field and provides a solid foundation for further study.