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Avis et commentaires pour d'étudiants pour Supervised Machine Learning: Regression and Classification par

6,766 évaluations

À propos du cours

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....

Meilleurs avis


23 nov. 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.


21 sept. 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

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76 - 100 sur 1,524 Avis pour Supervised Machine Learning: Regression and Classification

par Oscar A

2 oct. 2022

Súper bueno, estaba esperando este curso en la versión de Python y me encantó. Se enfoca en entender gráficamente cómo funcionan estos algorítmos de tal manera que uno pueda jugar con las variables y los datos para ver visualmente como cambia el proceso de optimización. Una vez se entiende gráficamente se procede a ver las matemáticas detrás de esos algorítmos, las cuales entran más facilmente luego de haber entendido el proceso visualmente y didacticamente. Súper recomendado.

par Dr. P N

31 août 2022

Very good structured and step by step explainations. The practical parts were not always easy but a very good proof of the individual undersanding.

After completion I will certainly have a look into the examples just for refreshing after finishing and gaining the full picture.

Very good, even my son studying AI did already know who the lecturer is: One of the best world wide.

What shall I say more? Learning from the best in the world is always a pleasure and a benefit forever

par Carlos J G

28 juin 2022

El curso es muy claro y bien dictado. Es me jor que el curso de achine Learning que estoy tomando también con NG. Recomendaría unos ejemplos mas trabajados y un curso previo de Python, pues esta es la parte que me costó mas trabajo. Aunque los ejemplos son en Jupieter, hay mucho software oculto que uno no puede entender y analizar. Lástima que por los costos no pueda continuar con los demás cursos, por eso quedo a la espera de la ayuda financiera.


Carlos J. Gorricho

par Théo D

17 déc. 2022

An excellent teacher, with high-quality (and addictive) lectures which really motivated me to continue into the domaine of Machine learning and Deep learning ! I highly recommend this course for everyone, especially for beginners who want to know more about the field ; the flow is nice, it's easy to follow at your own rythm and doesn't require much skills prealably, so very accessible. Thanks to Stanford University and Coursera for this first experience that I enjoyed a lot

par Thomas M

5 juil. 2022

I completed the Machine Learning course a few years ago and wanted to refresh my knowledge with this new speciality course. I think that this course was far superior. The use of Python in conjunction with the Jupyter notebooks isa big improvement over the Octave/Matlab usage in the first course. And my congratulations to the builders of the optional labs. They are extremely detailed and appear to have taken a lot of work in design and implementation.

Thank you.

Tom Mone

par Suraj B

20 juil. 2022

This is the best course for revising the fundamentals of Supervised ML. I enjoyed the thorough explanation by the course instructor Andre Ng. Many topics covered in this course such as Regularization, Overfitting, and Gradient Descent are building blocks for Deep learning(NN). The practical approach for the implementation along with the conventions stated by Andrew makes this course the best among all. Overall, I enjoyed a lot learning the first part of this course.

par Sudhakar V

11 juil. 2022

The course covers the basics, which help understand the concepts behind regression and classification.

The labs are in python, which makes it easier to follow.

Programming the cost function and many other functions manually instead of just using the library helps understand the concept.

Finally, the instructor Andrew NG is calm and composed in explaining the complex concepts and making them easy to understand.

I thank the entire team for coming up with this course.

par vinay k g

8 janv. 2023

Such a good course! I did try out some ML courses on other platforms, but this one really made me stick till the end. Also, I love coursera's grading system and the way it asks us questions after every lecture. It really keeps the learning cycle healthy. I loved Instructor Andrew's explainations and vivid example on most of the concepts. If anything, I love the fact that this course generalizes a lot of the concepts into some daily life examples.

par Namhoang

2 sept. 2022

Though the course is divided into only 3 weeks but I felt like completed a 4-week course, since there are many videos and practices(mostly optional). As usual, the explanation and instruction of Andrew Ng. is really amazing, it should feel like lots of abstract concepts and terms but he did a great job breaking it into smaller parts and show the learning how things are done. Thanks Andrew Ng. and team for making such a great course.

par Willard R

24 août 2022

This course is surprisingly excellent. I find the lectures by Prof. Ng to be easy to follow, extremely informative, and occasionally challenging. I do appreciate this newer version of the course that uses jupyter. I tried the older version with octave and I got bogged down dealing with the software installation issues, and never finished that version. I would highly recommend this course to anyone interested in machine learning.

par Mohab A

27 août 2022

The Course is really Awesome and it has improved too much from the old one, the graphs and the lab were very helpful and gave me the intuition of all the complex equations and models used in the course, Also the teaching method was really great for beginners and the instructor didn't jump on topics but was moving step by step, so overall i really enjoyed the course while learning and i'm looking forward to start the next course.

par Williamberg F

20 janv. 2023

This course is just the right way to get started with machine learning. Andrew's friendly way of explaining complex concepts makes it much easier to understand. The assignments aren't so punishing that you can't progress, but not so easy for you to pass them without actually getting a grasp of what is being taught. I've also found the tips in the practical assignments really helpful when needed. Totally recommend this course!

par Siva S

8 nov. 2022

This is a great course! Dr Ng takes you through the required concepts in a manner that makes it easy to understand. Of course it may sometimes take a couple of reviews to really get to the intuition of it all. Also I feel that knowledge of basic python is a must to do this course. I would not have been able to get through it without having that foundation. Can't wait to start the second course of the series! Thank you Dr Ng!

par Khiran G

20 déc. 2022

A best course for beginners , intermediate students. It's great that it contains labs on which one can work, play with it .

Labs are visually good to help understand concepts better and teaching by course instructor ANDREW NG is absolutely nailed to the ground.

Last but not least a recommended course to watch if you aspire to become ML engineer

--Thank you Andrew Ng sir and whole team who contributed in designing this course

par Amirhossein A

28 août 2022

Abseloutly amazing course, Andrew NG did an amzing job on teaching and also explaining concepts and formulas, it could get better if you shared some liks of usefull material about the theoretical machine learning for example I'd like to know why we use the linear regression model as the variable of the logistic regression model(sigmoid function). thank you for this amazing course sincerely Amirhossein Abolhasani

par Lena E

8 nov. 2022

Great explanations for all of these difficult mathematical formulars. I understood everything, but there is definitely  enough to repeat over and over again to really get it . Need a little break before participating in part 2 and 3, but I´m looking foreward to it!

I absolutely loved the exercises, it helped a lot to test and try out things and to see, how easy some parts really are, if you take them separat.

par Arnak P

22 juil. 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.

par Daniel W

29 juin 2022

Thought 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.

par Tobias K

1 oct. 2022

Very cool course, Andrew is not only a brilliant mind but also a natural teacher. After I have taken both this version and the previous (Matlab) version of the course, I feel that the assignments and quizzes are easier than in the previous version. However, thanks to the numerous Jupyter Notebooks provided, there is plenty of opportunity to dive into the matter in some more depth, and to learn something new.

par Sasa G

12 juil. 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!

par Shreyansh P

20 août 2022

A great course with well clarity of concepts. I have nearly no knowledge of most of the maths or statistics topics required for Machine Learning (such as Linear Algebra, Calculus, etc.) yet with time and determination along with some research of my own. I was able to fully understand and complete the course. The course breaks down certain aspects of Machine Learning very well making them easier to absorb.

par Irene P

3 juil. 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.

par Daniel A

29 juil. 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

par Mohsen F

17 juil. 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. :)

par Charles B

30 oct. 2022

The course focused on learning how to do the equations and programming for the algorithms. It gave a good understand of not just the algorithms but when and how to implement them. There was no time wasted on being concerned how to generate plots or gather test data for the assignments. That was done for us... which was a great help. All in all a very well planned and executed course.