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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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326 - 350 of 4,718 Reviews for Supervised Machine Learning: Regression and Classification

By Munene

•

Sep 15, 2024

Love, love, love it! Concepts are very clear, simply explained and easily understood for such a complex topic. Albert Einstein once said: "if you can't explain it simply, you don't understand it well enough."... and the course instructor is an embodiment of a true master of his craft. I'm going to complete the remaining courses. Thanks Andrew!

By Bernard C

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Jun 19, 2024

Course is really very well designed! Andrew is a great teacher and I am impressed by how easy it is to follow step by step in concept and able to do the coding in lab even without python programing background to start with. Will definitely recommend to everyone that is interested in Machine Learning, I am looking forward for the next course!!!

By K s

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Apr 9, 2024

Andrew's lectures gave me an insightful and accessible introduction to the intricacies of machine learning. His wit and positive attitude motivated me to accelerate my learning progress. I am truly grateful for the opportunity to partake in such an excellent and rewarding course. I am ready to take on the second course in the ML Specialization!

By SHIVANSHU U

•

Jul 31, 2022

Such a beautiful course I have ever seen about machine learning. No, one can explain like andrew Ng sir . He explain all the algorithm with mathematical aspect too. I can solve all the algorithm with or without sklearn library. Thanks for making these type of course.It is help to make a perfect root of student in the feild of machine learning.

By Rian F J

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Jul 26, 2022

The course is very good since the topic really explains the theory behind the concepts needed for machine learning. Andrew Ng also discusses the concepts very well and the lab assignments are very helpful to solidify the ideas you have to learn from the tutorial videos. I would definitely recommend this course, especially for beginners in ML.

By Valerie D

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Nov 3, 2022

Even auditing it, you learn a wealth of material from the videos. In fact, in some respects, not having access to the optional labs (you have to "upgrade", or subscribe, to gain access) motivates you to create your linear regression code from scratch. That makes it a bit more of a challenge and helps you work on your coding skills as well.

By Jaya K K

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Sep 5, 2022

Professor Andrew NG and his team did a great job yet again with structuring this course. Coming in with some background in Machine Learning, this course for me served as a great refresher for the introductory concepts in Machine Learning. I'm also delighted to take baby steps into python programming and scikit library through this course.

By Geethika I S

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May 24, 2023

Learned a ton. It's a bit to take in. Unless you have a math background I recommend taking the Mathematics for Machine learning course from Deep learning.ai. Anyway I can recommend this course to anyone who's trying to break in to AI. The instructor is the best. And the content is very well structured. Thank you so much DeepLearning.ai Team

By Abraham Y

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May 9, 2023

Andrew does a great job of simplifying complex topics into digestible bites for the student. I have taken other ML courses on another platform, and there, the instructions were merely how to use canned algorithms. I did not learn much there. This course explains some of the math behind the scenes and thoroughly explains the how and the why.

By hassan m

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Apr 21, 2023

After completing supervised machine learning, I get acquainted with fundamental of machine learning and learned about regression and classification algorithms and many other features and how to apply them on over projects. Of course, I want to keep on learning and reviewing materials and learn other courses to be a machine learning engineer

By Amir Z

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Jan 18, 2023

Andrew explained difficult mathematical equations in a manner that make it easier to understand the concept of those strange formulas and actually how they were developed. I believe Although we may not use those formulas in real-world problems but understanding the concept will help us a lot in understanding any machine-learning algorithms.

By José I H

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Jan 11, 2023

Muy buen curso! Logré aprender las herramientas básicas de conocimiento sobre regresión lineal y logística para luego continuar profundizando de forma independiente la librería scikit learn y otras que automatizan los procesos, pero es fundamental saber qué es lo que está pasando al interior! Muy buen entendimiento de los conceptos básicos.

By Singye D

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Sep 23, 2024

I found this course to be incredibly informative and well-structured. The explanations of both regression and classification techniques were clear and easy to follow, making complex concepts accessible. The practical examples helped reinforce my understanding, and I appreciated the hands-on projects that allowed me to apply what I learned.

By Sanjay N

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Apr 30, 2023

Prof Ng does a fantastic job explaining complicated concepts in a simple way. The coding requirements are not too egregious and help to solidify the concepts for longer-term retention. I felt that I learned the basics very well, and could furthermore explain them to someone else as well, which is helpful in my work in Product Management.

By Ishan G

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Apr 30, 2024

Concepts were taught in a very visual manner right from the basics which made them crystal clear to me. The optional labs were amazing and helped me visualize the topics covered really well. Having a basic knowledge of Calculus, Linear Algebra, and Python is very helpful to fully understand the formulae for models, but it is not required.

By Deshad

•

Apr 19, 2024

Course was very interesting to follow and definitely broadens the mind in the machine learning subject. Instructor walks through the concepts of supervised machine learning with understandable examples and great teaching techniques. definitely recommended. Thank you Stanford and Deeplearning for providing me financial aid for this course.

By Omid S B A

•

Aug 16, 2023

this course was really helpful in order to make a person acquianted with the concept of machine learning and it's uses. But it can be better, maybe its my mistake but in optional labs if a person delete cells unintended, there is no way for recovery. So by solving this bug i believe this course and similar courses would be more efficient

By Alexandru-Samuel D

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May 29, 2023

Awesome course. Lots of high-quality material to explain the concepts. Optional code labs to gain intuition and both quizzes and practical labs to strengthen what you've learned. Also, the updated curriculum puts high emphasis on real-world tools (Numpy, scikit-learn) and concepts like regularization, feature engineering, feature scaling.

By Ananda B

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Mar 24, 2023

This is my first Machine Learning course and I am happy that I successfully completed it. This course is very beginner friendly. The course instructor explains the concepts with great simplicity. The optional labs are very helpful. It felt awesome throughout the course and I am super-excited to join the next course of this specialization.

By CSE-52-Sagnik K

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Oct 12, 2022

Great and easy to learn experience throughout the whole course ,every part was explained in details. This is not an criticism ,but I had slight problem understading the gradient descent of logistic regression. Needed to take help from your old videos in youtube. But most of all Thank you for this course,can't help to continue the second

By Armin F

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Aug 23, 2022

Great course. This class gave me better perspective of supervised learning. Data examination, Feature scaling, normalization and gradient descent discussed in context of both linear regression and logistic regression. Underfitting and overfitting (more data and regularization as a solution) was clearly explained.

I liked labs a lot. Thanks

By Shimeng

•

Feb 8, 2024

I took a similar course in college and got a C, and I felt I never fully understood the concept. Now 10 years later, Andrew just makes it so much easier to understand the fundamental ideas of the concepts. Python and numpy also made it so much easier. I was able to blaze through and finish the entire course in just 3 nights. Thanks!

By SAMEER G

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Jul 15, 2023

It is a definitely great course. The Course Instructor Andrew Ng goes over the topics very well that even a layman can understand it intuitively. The mathematics for this course although not mandatory is actually useful to have some basic knowledge of statistics and calculus to understand the idea and methods behind the implementations.

By Chavhan Y N 5 I M S a T

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Jun 5, 2023

Great Learning!!

Pre-requisite : Basic python (it is needed to complete assignments and programming challenges)

Overall it is a great course to understand machine learning algorithms, including mathematical aspects of it. It also provides comprehensive explanation of Overfitting, Scaling, Normalization, Regularization and Sampling,etc.

By Satya P B

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Mar 29, 2024

It was really well design, interactive, and practice based course to learn Machine Learning. You not only learn theories, but also relates that to real world and write codes in practice lab. Overall it is a 100% recommended course. Even a beginner can learn and understand this course easily without any prior knowledge in coding, or ML.