Chevron Left
Volver a Supervised Machine Learning: Regression and Classification

Opiniones y comentarios de aprendices correspondientes a Supervised Machine Learning: Regression and Classification por parte de

2,165 calificaciones

Acerca del Curso

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....

Principales reseñas


16 de jul. de 2022

It is the Best Course for Supervised Machine Learning!

Andrew Ng Sir has been like always has such important & difficult concepts of Supervised ML with such ease and great examples, Just amazing!


4 de jul. de 2022

Andrew Ng is the best proctor for Machine Learning. The course has been perfectly balanced with thoritical as well as practical aspects. After this course I feel so confident. From ZERO to HERO

Filtrar por:

76 - 100 de 555 revisiones para Supervised Machine Learning: Regression and Classification

por Luca P

10 de jul. de 2022

All the fundaments explained at a slow pace, to help the learner to fix concepts in the mind. Of course, all the important passages are perfectly underligned by Professor Ng. He always give the feeling that teaching is a grat pleasure for him and this helps all the interested people to learn these fascinating subjects

por Nathan R

4 de ago. de 2022

The explanations by Professor Ng were very detailed and precise, which made it better for me to understand while reviewing the material. I also liked how this version uses Python to supplement learning, rather than Octave. The only thing I wished he would have touched a bit more on the math behind the algorithms.

por Vaibhav S

25 de jul. de 2022

Overall very good course for beginners. But I feel there is a lack of exercise. Some labs can have some small questions in between them and practice quizzes can have more questions also question could be more challenging in quizzes.

After completing the course I feel like relaxing on the J-w-b contour.

Thank you.

por Adri

27 de jul. de 2022

A really nice foundation course for supervised machine learning with right amount of math to understand concepts. I like how the course has been structured starting with simple concepts and slow progress to complex one .

The course contains python code lab which I found very useful to understand the theory.   

por Arnab C

7 de ago. de 2022

As a begineer in Machine Learning, I would suggest this course should be the first one every one takes. Mr. Andrew provides an indepth yet crisp insight of the underlying logics enabling us realise a ML model. Having a bit of very basic math background will help understand the algorithms far better.

por Ziaoulrahman S

25 de jul. de 2022

A very helful course for beginners the optional labs are tremedous and organised very professionally for beginners those who would like to learn Machine Learning. Background concepts are really well explained with real world examples. The whole course is a peice of Art!!!! Thank you Prof. Andrew NG.

por Sasi k D

24 de jul. de 2022

A excellent course for those who are into learnning machine learning. This course is explained in detail with wonderful examples which makes the process of understanding easy . And the optinal labs provided with course which have interactive examples gave a great intuition about the course topics

por Rahul C

19 de ago. de 2022

Course content and all optional lab are very helpful for the student who are changing their field from Any industry into field of Data Science.

All content are helpful and easy to understand.

Thank you coursera team and Deep Learning team to provide such kind of learning platfrom for biggner.

por Mohamed J

6 de ago. de 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 !

por Ovu S

25 de jul. de 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

por Anna V

7 de ago. de 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.

por rcotta

26 de jun. de 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.

por Abhay D

6 de ago. de 2022

This course is a great start for your Machine Learning journey.

I would say that you must have good knowledge of Python Programming as a prerequisite for this course.

Rest, the instructor is too good. He explains everything so well that you don't even need to learn anything.

por Shreyas N

24 de jul. de 2022

The course is very technical and hence gives importance to nitty gritty of the algorithms used. If you want to learn how the algorithm works and how and when shall we use feature selection or regularization for linear or logistic regression,then this is the course for you.

por Alireza H

11 de jul. de 2022

It was a really helpful course ,I've learned many useful concepts about supervised learning . practicing optional lab codes and Andrew NG's explanation was admirable, I'm going to enroll the next course in this specialization.

thank you COURSERA ,and thank you Andrew NG :).

por Yash B

11 de jul. de 2022

Really helpful! Doesn't feel that difficult in the beginning, but slowly starts to become a little complicated. Dr. Andrew's teaching pace and style is amazing, really calming. I enjoyed this course, and hopefully will love the next 2 courses in the specialization too.

por sichun z

6 de ago. de 2022

He uses easy and mimic words explains complex mathematical theories which is well understandable. The optional lab is especially well designed which helped me a lot to understand the concept of linear Regression and have practical experience with python.

por Girish Y

18 de ago. de 2022

Firstly i would like to thank Standford University for granting me financial aid for Supervised Machine Learning Course excellent and well oraganised weeks and all lectures were taught were clearly and even assingments were challenging as well .

por Rafał K

17 de ago. de 2022

Absolutely loved this course. I knew the basics of Machine Learning, so this part was a great reminder for me. Also, a big thumbs up for writing everything by hand - it let me understand gradient descent theory much more than imported library.

por A.D. J

21 de jul. de 2022

Prof. Andrew Ng is an amazing instructor with rich experience. I would like to be grateful to the entire team behind its realization. This course provides a balance between the theoretical aspect and the programming aspect. Highly recommended

por Shantanu

15 de jul. de 2022

The course covers detailed parts of explanations. Sir Andrew Ng and his team has developed this amazing course for learners. Algorithms are taught along with statistical content which can be hardly seen in teaching methods of any instructor.


9 de jul. de 2022

This course is just awesome. Andrew Ng gives you the underlying intuition of the two most popular supervised learning algorithms, linear and logistic regression. I got to understand and implement the mathematical models of these algorithms.

por Chad W S

4 de jul. de 2022

Better than the original due to the interactive Jupyter notebooks written in modern Python, than the Octave environment on the previous versions of the materials. And as before, Professor Andrew Ng is an amazing and engaging instructor.

por Goyo R

7 de jul. de 2022

E​s, probablemente, el mejor curso que he realizado sobre Machine Learning.

I​ntroduce de una manera muy sencilla conceptos centrales de la disciplina.

M​r Andrew Ng es un gran profesor, realmente he disfrutado mucho de esta experiencia.

por Ramesh B J

23 de jul. de 2022

I really appreciate the course instuctor and tutors for putting such efforts to simplify the concepts and algorithems to even understant the non background studnets in Machine Learning. I thank everyone who is part of DeepLearning.AI.