Understanding advanced learning algorithms never felt this convenient thanks to Dr.Andrew's. The content of the second course of this specialisation enabled me to gain a deeper intuition of how neural networks and decision trees work in the field of machine learning.

it was very good. especially for beginners who want to start their career in deep learning engineer it is the best one. anyone can understand the course if they have some prior knowledge in mathematics like derivatives, vectors and some machine learning algorithms.

It is a good course in terms of teaching concepts, but in terms of programming, the assignment expectations at the end of each week are somewhat inconsistent with the topics taught. The course teaches concepts well, but assignments don't align with weekly topics.

Excellent presentation of concepts and guidance on best practices. The instructor provides the right motivation for the concepts and the labs really highlight the finer nuances of programming these concepts. The material on ethics in AI was very much appreciated

best of the 3 courses in the specialisation but still let down a bit by the programming assignments. The original Machine learning course was more challenging when it came to graded assignments, this one less show with the hints not being hints but full answers.

Liked the explanations, steady pace and examples, clearly painstakingly learned places where students learn slower are addressed. Rather few labs in this one, but seem to cover main basics. Learned quite a bit, although taking as a refresher. Good update! Thans!

My favorite part was how beautifully the concepts from the first class, and more generally linear learning algorithms, are connected to neural networks. The mentors are so helpful, you feel like you have a private tutor teaching you advanced skills. Amazing!

I really enjoyed the course. Andrew is a great teacher. The examples were well chosen and it was not too complicated to follow. Good to take a look behind the scenes of what the state-of-the-art libraries are doing.

Also +1 on the the dad jokes at the end ;-)

When i started the Machine learning journey, i always had a doubt on this neural networks on how all this works, whether it is worth it to learn. After this fantastic course by Andrew, I am feeling more positive and motivated. Thank you Andrew and Coursera

Advanced Learning Rate on Coursera provided deep insights into optimizing learning rates for complex models. The course was well-structured and included practical strategies that enhanced my understanding. Highly valuable for advanced ML practitioners.

Another excellent learning materials on neural networks, decision trees, and model performance evaluation by Andrew Ng and his team. Andrew has a way of teaching difficult concepts for us to understand, especially the machine learning math intuitions.

The theory and concepts were explained really well. I however believe that if we could have video sessions for labs as well, it would have been better. Overall it was an amazing experience and I got to learn a lot of core concepts of Machine Learning

An excellent course that gives me the foundational and most important concepts on deep learning, this course shows me that the deep learning isn't a black box that only a few people understand, i can understand and i can innovate in this field also.

This course allowed you to acquire robust knowledge about neural networks, their metrics and qualitative analyses. Furthermore, it was possible to learn about other alternative learning algorithms such as decision trees, random forests and XGboost.

Another excellent class by Prof. Ng. I very much appreciate the clear explanations, careful introduction of concepts to ensure they build on each other, and even his jokes. My thanks to him and the whole team that built this series of courses.

absolutely awesome. This course and the specializatio is must to get into the mathematics of machine learning, Andrew Ng sir has a unique style of teaching and holds you right through the course. This is must to know how machine learning works.

It would be better if the notebook environment of the labs could be provided for us to download or for reference, as many of the output graphs could not shown on our jupyter notebook after we downloaded them due to missing of certain packages.

Another fantastic course by Andrew Ng and Stanford/DeepLearning.AI. Focus on neural networks and decision trees, with great practical advice for building machine learning systems in a real-world setting. Can't recommend these courses enough!

It is fascinating course! I want to thank Andrew Ng and whole team for very good explanation. Everything was clear, and especially week 3 is full of useful advices. I really enjoyed during this course. Thanks a lot. I am waiting for course 3

Great pedagogical skills. Andrew makes sure to keep the pace sustained, but each notion is clearly explained. I'm complementing my learning with some competitions on Kaggle to make sure I put into practice some of my newly acquired skills.

The content of the course is pretty good and interesting for someone with zero or basic knowledge in the field. Some basic level of programming is required but not that much. You get more from this course if you follow the specialization

What I like most about this course is that it took me step by step through equations and algorithms. It gave me deep understanding of different machine learning algorithms and the practical issues I can face when applying these tools.

This course is incredibly well-structured, have great exercises that are practical and go along the videos very well. Huge respect for Dr. Andrew Ng and all the participating members who contributed to the development of this course.

Great course! I don't see how anyone can launch into machine learning without Andrew Ng's course from Stanford University. I am grateful for the lessons and the team that put them all together! Thank you very much! God bless you!

This course has given very good insights into various supervised learning alogirithms like neural networks, optimization of neural networks, tensorflow adoption, How to address issues of overfitting / underfitting and Decision trees.