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

2,084 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:

51 - 75 de 531 revisiones para Supervised Machine Learning: Regression and Classification

por Arnak P

22 de jul. de 2022

Excellent course. Thanks to organizers, managers, lecturers, developers, etc. It was very interesting, very funny and very helpful. I am a senior data scientist and delivering ML lectures in different universities. In spite of that, I have found this elementary course quite valuable. It is always recommended to rethink known topics and ideas and see how other specialists are delivering those important concepts.

por Daniel W

29 de jun. de 2022

T​hought it was great and felt it was much more beginner-friendly than the previous course. The programming aspect of it can be tricky if you've never had programming experience, so I highly recommend you learn the basics of python (variables, for-loops, functions, etc.) before taking the course. If you have some brief background in ML and programming you should be able to finish this course relatively quickly.

por Sasa G

12 de jul. de 2022

It's a great course for the beginners in the area of machine learning. You should have some Python basics. Optional labs are great and you can learn a lot alone, if you have desire to investigate a bit their implementations. Final graded labs are also not so hard. I would maybe add more question to quizes and more exercises, examples and datasets. But it's, as said, still a really great course and thank you!

por Irene P

3 de jul. de 2022

With some Python experience, this was super hands on and easy to understand. I came into this course without a strong knowledge of how to decodify math algorithms, and with Andrew's super clear explanations and the super hands on optional labs, I found myself able to see how the alorithm was changing through visual graphs, and become able to apply the machine learning mathematical algorithms into code.

por Daniel A

29 de jul. de 2022

This is really a great course. Andrew Ng showed really great understanding of the and he was able pass it on by breaking the topics into atomic units. The labs were helpful and the quizzes were easy also. However, I would suggest that a complete project should be given and the whole code should be written by learner which can ensure they course was fully understood and further enhance their portfolio

por Mohsen F

17 de jul. de 2022

This course was really good. The visualizations in the lab were really creative and insightful. By the way if felt like in the third week, the speed of teaching stuff began to increase, It was ok but i was shocked at first. I am a teacher myself, so i realize how much this team worked to prepare this content. I want to thank all members of this team one by one. I hope i can meet them soon. :)

por Fredrik Ö

3 de ago. de 2022

H​ad already completed the old course "Machine Learning". Took this course because of the switch from Octave to Python. So i thought it was a great idea to repeat what i had learned and at the same time sharpen my skills in Python. Really liked the enhancements, like the extra optional labs with Scikit. Also this was a preparation for me since i intend to take the 2 continuation courses.

por Jeffrey C

14 de ago. de 2022

Terrific introductory course, but I wish it gave you the option for more hands on implementation of the supervised machine learning algorithms as you progressed. I could have easily passed this course with knowing the bare minimum, but I wanted to become proficient in the foundations, and unfortunately there wasn't much in the way of testing your knowledge without the training wheels.

por vijay s

14 de ago. de 2022

I felt after learning this, that my overall understanding has become very deep and now i feel very confident about implementing this in real life scenorio. It has given me clarity on "how to steps in Machine learning" . Very intutive and natural course for topic of vast calibre and application. Thanks to the team of coursera, and standford for sharing such information.

por Badavath T

5 de jul. de 2022

This course is fantastic, everything from the previous course but more. Adding Python instead of octave/Matlab is excellent, and the programming assignments are also beneficial. The teaching is exceptional as always. If you are looking for a course in machine learning, this is the best pick. I enrolled the day the course was released, looking forward to completing the specialization.

por Argha B

28 de jul. de 2022

Andrew Ng is one of the pioneers in the field of AI. His original course, while very theortically enriched, was showing its age for the choice of its programming language. This new specialization was just the right thing for someone like me who needed to implement all the concepts in the de facto language of AI, all the while learning the said concepts from the leaders of the field.

por Saif U R

16 de ago. de 2022

Thank you Prof. Andrew, Eddy Shyu, Aarti Bagul, Geoff Ladwig, and all the members of the team for a wonderful course. It is very easy to understand and, at the same time, enjoyable. And, deeplearning community is also very supportive. I got stuck several times in the course and the community help me to go through that. Highly indebted to all of you. Hasta la vista in course 2.


22 de jun. de 2022

Really learned a lot of mathematical concepts behind machine learning algorithms in depth. The course content is in sequence andintroduces complex topics in a quite simple manner. The associated optional labs and programming assignments hep get better understanding of underlying concepts. Nevertheless, the pre-requisites such as python, statistics are important.

por Shivanshu U

31 de jul. de 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.

por Rian F J

26 de jul. de 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.

por Jayneel S

9 de jul. de 2022

It was a brilliant course and it helped me think more analytically. The practice labs and visualizations were spot on, they really helped in getting the intuition about what was being taught. Andrew Ng as usual was really great and explains everything quite nicely and calmly. The examples given by him complement the theory nicely.

por Todd H

8 de jul. de 2022

Extremely impressive content (video, labs, and supporting interactive visulations) that truly help build intuition for the problems solved, while simultaneously aiding understanding of how and why to code the functions to solve the problems. Light on soley math, light on soley code, heavy on the intersection of the math and code.

por Diana K

10 de jul. de 2022

I am very happy to finish this course! It is the best! I took previous course, but matlab was quite complicated and new for me. So I am very glad that course has been updated with python. Everything was explained in details. This course highly motivated me in studying. Thank you a lot, Dr. Andrew Ng! You are very great teacher!

por yiping w

21 de jun. de 2022

A great learning journey with Andrew Ng and thanks to all of the people behind to make it so intuitive and fun to learn .. I never thought that ML could be such easy to understand and with the this new Jupyter notebook and all graphics and animations this course turns the boring math into an excited exploration into the future.

por Abdul J K

24 de jul. de 2022

The best course with exceptional strategy to teach through a marvellous instructor (Andrew Ng) has totally caused a pronounced impact on my learning and teaching skill. I appreciate Coursera for such an exceptional support and opportunities for learning. I hope I may payback in future through my expertise as well.

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.