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

4.9
stars
24,873 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

JM

Sep 21, 2022

Specacular course to learn the basics of ML. I was able to do it thanks to finnancial aid and I'm very grateful because this was really a great oportunity to learn. Looking forward to the next courses

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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451 - 475 of 4,868 Reviews for Supervised Machine Learning: Regression and Classification

By H L

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Mar 8, 2024

I loved the video lectures. They were simple but no compromise on quality was made. The labs tested only what was relevant to the course objectives. All the redundant stuff was handled by default. TLDR: The perfect course for someone with only basic knowledge of programming and mathematics

By Md. A I

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Dec 10, 2023

This is a great course for me. Two of the supervised ML problems Regression and Classification are discussed in sufficient depth, the building units of ML model and the mathematics behind them. I want to show my gratitude to Andrew Ng and other members associated with this amazing course.

By Prajwal Y P

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Feb 24, 2023

Very very very useful!! Neatly explained the 2 supervised machine learning techniques(Linear Regression and Logistic Regression) and with simplification techniques such as Scaling, Regularization, Vectorization and so on, beautifully explained. It is a resourceful course for ML beginners.

By Mohamed J

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Aug 6, 2022

This is the best course ever on Machine Learning (I hope it remains so in the future) . It was a honour to learn from the great Andrew Ng Sir. Thank you so much sir and to his team for creating such a great course. This course provides a great chance to #BreakIntoAI. I love this course !

By Tanya S

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Jul 2, 2023

The best course to get started with machine learning. Andrew Ng has done an amazing job of explaining these topics in such a simple way. The optional labs have also been very helpful. Also, the programming assignments and quizzes have been very helpful in understanding these concepts.

By Andrés R

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May 12, 2023

This course is killer. This is not the first time I'm doing this. The first time happened like 10 years ago when the course was given with similar approach and the labs were in Octave. It's super nice to see new material and wonderful to understand things with a different perspective.

By Niket S

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Jul 3, 2024

very informative and detailed; was able to learn and gain in-depth understanding of ML concepts; Improvements: it would have been good if the videos went deeper into the mathematics such as deriving formulas using calculus and explaining briefly where do the functions even come from.

By Vijaykrishna V

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Sep 29, 2023

Andrew Ng is the best teacher of the Machine Learning and AI concepts. Coming from a biology background, I really enjoyed Andrew's video lectures which explain some of the complex ML equations such as cost function, gradient descent in regression and classification in simpler terms.

By Marc S

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Dec 3, 2022

This course was incredible. I particularly loved the "intuition" training on the algorithms, so that one can actually see what is happening and how to best adjust things. I think Professor Ng might be the best online instructor I have ever come across. Thank you so much for this!

By Rudra S C

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Oct 10, 2022

Great course for beginners to Machine Learning. The vastly updated codebase to Python will be very useful for practiioners. The interactive model fits really gives a hands on exploration to beginner Data Scientists. Would be great if you can make the slides available for reference.

By Ovu S

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Jul 25, 2022

this course is by far the best i have ever taken , both physically and online, and one of the most important aspect of the course if you ask me, i will say it's the optional lab part of the course, because that's where you actually know how to bring the learning algorithm to life

By Wojciech S

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Nov 4, 2022

Very well explained material on linear and logistic regression (with some additional aspects like regularization, overfitting and underfitting). Knowledge is built step by step, which allows you to consolidate and organize the material. It is worth learning the optional exercises.

By Ganael D

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Feb 15, 2024

After completing this course, I have confidence in my ability to handle supervised machine learning projects. It was great! Andrew Ng is a fantastic teacher, everything was clear and easy to follow. I absolutely recommend this course to anyone who wants to learn machine learning!

By Khondaker H M A

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Nov 13, 2022

If I had to review it in a sentence, "This is the best course to get started with Machine Learning in the internet." I tried to learn machine learning earlier but failed. But this time Alhamdulillah I was finally able to understand the things. It will be suggestion to everyone...

By Shawn C

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Aug 5, 2023

An inspiring and exceptionally well taught course. In particular, despite the underlying sciences that culminated this topic, anyone can get started and learn many of the concepts, due to the quality of the instructions, to be inspired and start the journey in Machine Learning.

By Anna V

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Aug 7, 2022

Very clear, concise lectures and materials that were easy to follow. Enjoyed Professor Ng's enthusiasm and encouragment throughout the series. Most liked being able to watch videos and complete work on my own schedule, and also rewatch videos to understand better and take notes.

By Kshitij D

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Oct 27, 2023

I love the way this course is taught, and am very grateful to Stanford and Coursera for making it available. The labs are just amazing - rich with opportunities for building programming skills while also getting the concepts better encoded into our brains in an unforced manner.

By Chanvir

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Aug 23, 2023

This was my first course on Machine Learning , It was easy to understand. With lab visualisation I got clear of every equation which were hard to understand . Each video has one topic and a quiz later to which helps to check your understanding . Thanks Andrew . Thanks coursera.

By rcotta

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Jun 26, 2022

Great course! Provides a very good understanding on how some of the supervised learning algorithms work and makes you code a bit in Python to bind theory and practice together. Ng's explanations are very clear and I had a relevant increase on my knowledge after completing this.

By Wai A

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Jun 1, 2024

Honestly one of the best course I ever took,the instructor is well explained.This course really understand me about machine learning because I have taken other ai courses but I don't really understand that courses but this course is definitely worth time.I highly recommend it.

By Johann P

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Apr 25, 2023

Great course! Andrew Ng makes sure you as a student can digest relatively complex subject matter more easily because of how he breaks it all down in a simple manner. Additionally, his positive attitude and well-thought teaching makes the course an enjoyable experience. 10/10

By Temesgen Z

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Dec 13, 2022

This course is very well organized. It is online, you don't need to be at college to take it. You can take this incredible course anywhere and anytime. Take this opportunities and let your knowledge shine the world. I don't know how to thank the Coursera Initiative community.

By Kwabena K A

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Oct 13, 2024

An amazing course! Very well-structured, informative and easy to understand. I would highly recommend it to anyone interested in beginning to learn machine learning. This is the best and most simplified introduction to machine learning you could hope to get. Enjoy the course!

By Marc G

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Sep 25, 2024

Hands down the best course on the whole web for Machine Learning. I have taken courses by IBM, HarvardX, and many many others, and none of them are as clear and well explained, with enough variety of labs, quizzes, reading and videos as this one. Take it! You won't regret it!

By Aditya S

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Jul 9, 2024

This course has really helped me with practical knowledge. All the formulas seemed logical and running them live on the LABs really helped me build my confidence with the same. I however would've also liked to see Naive- Byers Classifier and k NN classification in the course.