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

6,923 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


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


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.

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26 - 50 sur 1,550 Avis pour Supervised Machine Learning: Regression and Classification

par Alireza S

19 juin 2022

This is a great Machine Learning course for the first-time learners offered by the best in the field. IMHO, the focus of course is on learning the underlying theories of machine learning rather than short-circuiting the basic concepts to the helpers libraries developed in Python.

par Dingrui W

26 juil. 2022

Brilliant course! I really enjoy the journey and cannot wait to start the second course. It's such a great thing to have a course like this which is made with great endeavor. And spending time and thoughts on it is even more amazing. I am so lucky to encounter this course!

par Pritam D

30 juin 2022

Perfect balance of application and theory, and wise choices in ramping up the complexity gradually. Discussion boards are very helpful, feels very much like personalized learning. Thank you!

par Dan C

23 juin 2022

Excellent course, very logical and well structured. Highly recommended to anyone interested in learning about this topic. Assignments are on the easy side but you learn a lot nonetheless.

par Vishnu

24 juil. 2022

This was a great course to understand all the math and logic that goes behind some of the most commonly used ML algorithms. Interesting and a great start to the specialization.

par Ryan M

25 juin 2022

Good for beginners. If you have taken the previous online course 'Machine Learning' taught by Prof. Andrew Ng, you may find this course much easier.

par Mohammed A B

24 juil. 2022

One of the best ML courses so far. The Course is well designed and very well presented by Andrew NG. I highly recommend it.

par Abhishek P

20 juin 2022

Precise explanation of the fundamentals of Machine learning techniques, using mathematical examples and python.

par 马镓浚

7 août 2022

Very friendly for beginners, a good refresher if you already had the knowledge of machine learning.

par Alexander S

17 juin 2022

- Amazing instructor

- Very clear and easy to understand examples

par Kahouli M

24 juil. 2022

ilove how simple and rich this course is

par Will S

15 déc. 2022

Pretty good introductory course! Personally, I would like to see more time devoted to the Scikit-Learn implementation (and maybe Pandas data frames instead of NumPy arrays for the training data) as opposed to hard-coding the algorithms and using really small data sets. Scaling upwards and using those libraries on larger data sets should be relatively easy after you nail the foundational concepts in this course, though. There is definitely something to be said about knowing the mathematical algorithms running in the background of these black box models, and this course does a really good job of explaining them (namely, cost functions and gradient descent).

Apart from scripting these algorithms in Python code, the course is somewhat lacking when it comes to conceptually explaining regression and classification models. For example, there is no time spent explaining how to interpret regression model coefficients and intercepts, and there is little time spent explaining the probabilistic interpretation of the sigmoid function and the importance of choosing a good decision boundary. It is one thing to know how to program these models and another thing to be able to explain them to people without a technical background, which I think could be a good lesson in future versions of the course.

Overall, great introduction to the models and their implementation in Python! I would absolutely recommend the "optional" labs throughout the course (especially if you're new to Python) because they show you the code that you'll have to write in the required assignments.

par Yu L

29 juil. 2022

Very clear and intuitive explanation with a great instructor, though the contents are a little too easy, especially for people with a STEM background. More exercise could be set with less guidance (currently it's like writing ten lines of codes for every week of learning). Also, it would be nice if there could be an exercise dedicated to the use of packages like scikit-learn in depth, since that is what most people will end up using the most.

par Kostas M

5 juil. 2022

A very good introduction to Machine Learning. I would prefer some more math since this gives me more confidence in understanding, but the course is aimed to a wide audience so that's acceptable. I accompanied the course with Andrew Ng's notes on machine learning.

par Gariman S

11 juil. 2022

Andrew sir's teaching made the course interesting and exciting. However, the course was too easy and some more mathematically oriented discussions could have been done.

par Mubeen u h

2 août 2022

very good course

par Alejandro D S g

2 sept. 2022

The course is good but once you cancel the subscription, you lose access to the codes. I think that should be change.

par Mikhail B

14 nov. 2022

I have completed this course in full and as a result, I am highly satisfued with how Professor Andrew Ng explains the materials. Thank you for this! However, I cannot understand, why after completing the course a part of studying materials are not accessible, even though I paid a sufficient price for the course. These unaccessible materials include Python programs which were used as a practice. Frankly, I find it unfair, since this practice would be extremely important to revise the materials while improving my skills in Machine Learning in the future. Moreover, a part of the montly fee was paid also for the practice materials. I may agree that these Python programs can be private, however,there should be ways to overcome this issue. Without the possibility to revise the code it will be much harder to create our own applications and programs.

par Flavia B

18 oct. 2022

I feel like this course tries it's hardest, that everyone can follow it. But because of that it doesn't really dare to go deeper than just give an overview of machine learning. The tests are way too easy to pass with 100% and you can't really write your own algorithms afterwards. Also most of the examples are with one variable, so it's easier to follow, but it would be much more helpful, if we could see more complicated and real live examples.

par Darshan H

5 août 2022

Unable to Open the labs and submit the lab assignments

par Nazib E E K C

5 juil. 2022

Brilliantly Designed course to teach beginer on Machine Learning. The course focuses on the theory behind machine learning. The content convered in the course allows the student to get an intuitive idea behind machine learning and gives him an idea of the mathematics behind it. The course is not very math intensive, but there is just enough math covered here to give the student an intuitive idea of machine learning.

The coding labs provide very detailed code, which the user can learn and analyze to make his own machine learning algorithm

My favorite part about this course was how neatly the jupyter notebooks and python files of the lab were arranged and provided. These lab files take the burden of coding from scratch away from the students, and allow students to focus only on the algorithms behind machine learning.

After this course, machine learning codes will no longer be a black box, but will be something you will understand very well. So, after doing this course, the next time you use Machine learning libraries like SciKitLearn, you will know exactly what is going on behind the curtains, can you can adjust parameters of ready-built ML funcitons to fit your needs.

At the end of this course, you will learn how you can modify machine learning codes for each custom need, and you will gain the ability to do those modifications yourself. After this course, you will be able to write specific machine learning codes which are well suited for a different specific application

par Shaun S

17 juil. 2022

The course is very easy to follow, building slowly enough and with enough examples that it's usually simple to understand, and then, looking back, you discover that you have learned something quite complicated. I have enough basic coding experience in python to handle basic functions such as those in this course already, so I found that part quite easy; this may not be the case for those with no python background at all.

Andrew Ng has a great teaching persona, and it's a real pleasure to watch the videos, even aside from what I'm learning, just because the vibe is so cheerful and supportive. As an educator and teacher trainer, I can be quite critical of how courses are taught, but this one is just a joy. I feel like there's a lot for me to learn from Andrew about teaching.

The only (minor) quibble I have is that the final lab is a bigger jump in difficulty than I was expecting, but there is definitely enough help provided within the lab itself that it's still doable.

par ian

11 déc. 2022

If you a newbie in the field of Machine Learning and would like to find the bible of Machine Learning with being detailly instructed, then this course/specialization is absolutely made for you. I love the philosophy of teaching from thay Andrew Ng in a way that he always take all the technical concepts & notations and explains them in math-neutral manner as much independent from math as possible, unlike many other courses which heavily have math terms required for understanding the content. In addition, he guides us always with a question first in mind that is this concept/formula crucial for this purpose, if not, then we skip for now (the master of abstracting the nitty-gritty) enabling me generalizing the whole picture while maintaining a practical orientation approach in both optional and graded lab assignments. A grand appreciation for his great contribution on instructing those content more approachable to the wider set of learners of diverse backgrounds.

par Dinesha K V

18 août 2022

This is an excellent course on supervised lachine learning. The programming assignments are in python.

I have completed the previous machine learning course (programming in Octave ) by Andrew Ng hence I was comfortable with the concepts.

I was new to python and Jupeter notebook. Python implementation part (programming and explanation) is very friendly. I sincerely thank the mentor for immediate help on my problems in programming.

I comleted all assignments succesfully. But the strength of this course is also in the programming material given.This material is comprehensive, very rich and extremely useful. I need to go through in detail. I feel going through course material will help me to be comfortable in reading, writing, developing python programs for ML applications.

A big thanks to Professor Andrew Ng, Mentors and the deep learning community.

I strongly recommend the course for everyone interested in AI/ML.

par Vaibhav M

14 oct. 2022

Amazing courses that go into detailed explanations about the math and intuitions behind the algorithms without getting too convoluted or making things unnecessarily complicated just for the sake of it.

Prof. Andrew doesn’t just tell you the name of a function for a library (like scikit

learn or tensorflow) and give you magic numbers for parameters. You actually build the model yourself and learn what the parameters stand for and what is the purpose of those parameters and hyper-parameters.

The specialization is well divided into meaningful courses and each course is well structured so that you know exactly what you are going to learn and what key specific skills you will get after completion of a course. Because of the quizzes and practical labs, after completing a course you actually gain confidence that you can design optimized solutions for that particular set of problems.