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Вернуться к Supervised Machine Learning: Regression and Classification

Отзывы учащихся о курсе Supervised Machine Learning: Regression and Classification от партнера

Оценки: 233
Рецензии: 57

О курсе

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....

Лучшие рецензии


22 июня 2022 г.

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.


24 июня 2022 г.

absolutely amazing course, coding assignments are designed perfectly and the course helps in understanding the working and the math behind the algorithms which makes it so recommendable.

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51–66 из 66 отзывов о курсе Supervised Machine Learning: Regression and Classification

автор: Süleyman M P

18 июня 2022 г.

It is very basic and understandable course

автор: Henrik S

27 июня 2022 г.

I​t was everything I wanted it to be!

автор: Tom J

25 июня 2022 г.

T​op notch. Superlative.

автор: Pak Y H

22 июня 2022 г.

Best online course!

автор: Gabriel R

20 июня 2022 г.

Very good course :)

автор: killian p

22 июня 2022 г.

Very well taught !

автор: Fernando A

28 июня 2022 г.

Excellent course!

автор: Mitchell C

21 июня 2022 г.

Amazing course!

автор: Michel M F

28 июня 2022 г.

Great course!

автор: Trang Q K

27 июня 2022 г.

Great course.

автор: Vuk L

24 июня 2022 г.

Andrew Ng surpased himself as far as his teaching skills. I am amazed by quality of his lectures and the way he explains things. However I found that quizes were to too easy. One should just pay attention to what was said during lectures and 100% grade is guaranteed. That's why I'm giving 4.0, although I think 4.5 would be more appropriate. All in all - great first course!

автор: Sreeraj N R

26 июня 2022 г.

a great course to understand theory of supervised machine learning. Need lectures for numpy and scikitlearn

автор: Royston L

21 июня 2022 г.

I don't understand why the practice lab code for gradient descent and the lab assignment code is different.

автор: Faizan T

23 июня 2022 г.

Vectorized implementation in the assignments would have helped

автор: Mykola S

24 июня 2022 г.

a​ bit more complicated tests will be good

автор: Adnan H M A M

25 июня 2022 г.

In general, I think it was a valuable course to take. I like the way Andrew tried to conveying the ideas intuitively to make sure the students understood the methods behind the learning algorithms. However, I would've loved if there was more in-depth treatment for the Math aspects of the obtained results. Also, the assignments + Optional labs were not as engaging as I hoped. What I mean by that is, it almost required no deep thought from our side to implement the procedures. In other words, there was a lot of skeleton code that makes you "implement" the algorithms with almost no thought (which I don't think is beneficial to the student's learning experience)