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1,850 Bewertungen

Über den Kurs

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....



16. Juli 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. Juli 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

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101 - 125 von 468 Bewertungen für Supervised Machine Learning: Regression and Classification

von Aravind S D

6. Aug. 2022

It's a refresher for me and I learnt from one of the best teachers in the world and I admire to do the same and pass the knowledge. I can't forget who taught me the best and that's Andrew ng. Thank you Andrew ng!

von OM B 2

28. Juli 2022

Excellent course!! must be taken by an AI enthusiast. The concept is explained in a clear and crisp manner. The content is organised in such a way that while going through it, we will master concepts step by step.

von rahul k

17. Juli 2022

Topics covered are very useful and can be implemented in real life. Clear and Precise explanation of each topic. Pace of this course is quite good. Visualizations made it easy to understand the basic principles.

von Boyang H

15. Juli 2022

Although This should be deemed as an Intro Intro Course to Machine Learning, you can not really find a better way to dive into the field. Andrew Ng is one of the best professors you can have in your whole life.

von Daniel J R B

14. Juli 2022

I liked this course, very didactic and with many practical labs where you can experiment and understand all the math behind the ML models, and also learn all the basic programming to implement them using Python.

von aakash b

23. Juni 2022

In the world of today when most people are pursuing courses for an embellishment of their CV, this course shows that the best minds are those which focus only on learning without worrying about the consequences.

von Devinder K

11. Juli 2022

Very well designed. I am glad that I joined this course. Video embeded questions and quizzes really helped in clearing the concepts.

Lab practices were an added bonus to pace up our learning.

Thank you Andrew Ng

von Samuel Q

2. Aug. 2022

Lectures are very well made. Support material (jupyer notebooks) is excellent. Awesome course. Great way to brush up on the basics and its also nice to hear Andrew's enthusiasm and passion for machine learning

von Vardan M

8. Juli 2022

This course is next level gives all the intution about machine learning algorithms including linear and logistic regression and also go throug one of the most popular machine learning library named Scikitlearn

von Bruno P

29. Juni 2022

Great. For me, this course was a personal milestone, where I was able to understand the mathematical roots of machine learning and at the same time its implementation without necessarily decorating libraries.

von Anton

1. Aug. 2022

Great course for complete beginners. I would recommend something different for people with technical backgrounds though - this one is too simple (although I do understand that it is not a bug, but a feature)

von Ashraf H

26. Juni 2022

I​t was a great learning experience! Thanks to Dr. Ng and to everyone who contributed to creating this wonderful course. The content was very accessiable and made learning such a complex subject, quite easy.

von Petro S

1. Aug. 2022

First of all t​hank you for possibility to study this course.

Material is presented in a clear and understandable way.

The examples presented are clear.

Liked the interactive possibilities of laboratory work.

von Ahmed H F

24. Juli 2022

an amazing hands on experience, you not only learn ML but also python on the go and it is a very amusing course and very short to the point, easy to finish with some little effort and concentration on it.

von Jeff W

2. Juli 2022

I started taking the old version and then I heard this one was coming, so I did the new one instead. Much better! Great to be in Python and even clearer lectures. Prof Ng is easy and fun to listen too.

von Katerina A

28. Juli 2022

It was the best course I ever had! It iwill be very difficult for me to use the algorithms I was tought, but I am very thrilled that I finished it! I am grateful to Professor Andrew Ng and the team!!!!

von Sebastian A

9. Aug. 2022

I really liked it! Andrew explained perfectly all the concepts and I understood everything.

The only thing I think It could improve is to have more Lab Tests, so you practice more what you have learnt.

von Mahdieh E

21. Juli 2022

this course has helped me become familiar with machine learning concept, it is a great opportunity to learn machine learning algorythms, for everyone with a basic knowledge of Math and Statistics.

von Ciro M P

1. Juli 2022

G​reat course. Easy to understand but deep in contents. Accesible even if english is not you mother tonge, but better if you have certain math base knowledge. The Jupyter notebooks are so useful.


17. Juli 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!

von Javed A

5. Juli 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

von Kyaw N W

28. Juli 2022

I​ started with onld ML course last year, completed successfuly but did not purchase the certificate. As I am more familiar with python than Octave, this new course make thing clearer for me.

von Pritam D

30. Juni 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!

von Anupam D

3. Aug. 2022

I​ like the way jupyter notebooks are designed. It saves time on testing the code because it displays step by step what is happening and allows me to focus on the code logic implementation.

von Gabriel T

3. Aug. 2022

Amazing course explaining the math behind the most famous algorithm. Good explanation of the gradient descent.

Thank you to Andrew Ng and his team! A bit of math background is needed though