This course contained much more in-depth information than I expected. The programming assignments were very challenging. I learned a great deal about machine learning and the underlying math used in neural networks. I would definitely recommend this course to anyone who is serious about gaining knowledge in the area of Machine Learning.

This excellent course allows you to gain fundamental knowledge of the basics of neural networks, including mathematics, exactly to the extent that is sufficient for understanding, but which does not require being an expert in mathematical analysis and linear algebra. The course inspires experiments and inspires self-confidence! Thanks!

Andrew does an amazing job of explaining complex concepts in a simple way. I had a basic understanding of neural network architecture before taking course 1, but I had a lot of questions about why certain things are done. Andrew's lectures and the discussion forums helped answer a lot of those questions. Looking forward to course 2

Excelent course!, not for biginners. Very hands-on and with low previous requeriments, only to know plain python. But, it will not be easy if you don't have programming experience, some exercises are a little complicated. Also it will be good for you is you are familiar with algebra and calculus, you will see lots of matrix operations.

Un formidable cours offert par Andrew Ng dont les explications sont remarquables comme toujours. Une bonne introduction au deep learning que je conseillerai a tout personne souhaitant faire une carriere en deep learning. Les devoirs sont d'un niveau assez eleve, mais avec un peu de concentration, on arrive a s'en sortir. Great Course!

It's been an exciting journey so far, learned a lot of new concepts like python broadcasting and vectorization. Andrew Ng makes it easy to understand such complex concepts.

The assignments are pretty aligned with the lectures and well structured. They help me comprehend the concepts step by step.

Looking forward to remaining courses.!!!

Very complex processes were explained in a way to make it easier to understand and implement. The repetitions of important concepts (e.g. matrix shapes for different steps) helped me to retain the material much better. Andrew Ng is a wonderful teacher, and I will definitely be taking the rest of the deeplearning.ai courses on Coursera.

First course was very good. I also took Dr Ng's prior Coursera course which I also liked. This one focuses more on Neural Networks and tries to demystify backprop -- does a fairly good job, but it takes some focus. It is also great the HW and examples are in Python and on notebooks which is more practical.

Highly recommended course.

I was nervous going into this course that I wouldn't have the necessary math background to keep up. However, the course covered all the requisite math and I was fine. I found it to be a very enlightening course for a technology manager like myself. Now I feel more confident discussing Deep Learning projects with my engineering team.

Brilliant! I really enjoyed this course, it was really good to take a real in-depth look at Neural Nets and how they work. Great for building intuition on the subject. I also loved the interviews with some big names in the field, they were really informative and have given me lots of avenues to investigate and explore in the future.

A great course to get started with Machine Learning and AI. If you haven't done linear algebra and calculus at university, you might struggle a little with the maths behind it, but can still gain enough insight to how it works, you just have to trust the equations given to you. Get ready to do Hello Cats! (the hello world in AI world.)

Great course, from the basic concepts to implementation and practical tips. Less math-heavy and less conceptual than Prof. Hinton's, but more practical and focused on engineering. Highly recommended. Not being a computer scientist, and with my coding skills a bit rusty, I was grateful for what some call "Python notebook spoon feeding".

This is one of the best course I have done, the course contain in-depth information regarding deep learning and the programming assignment will help you, see inside how deep leaning algorithm works. It is best if you do machine learning Stanford course before doing this course, but it is not required, anyone can start with this course

This was the best experience ever! Everything was explained in detail, everything was very straight forward, forums were amazing. I always had a lack of self confidence in coding, I thought that I don't know enough to become an AI researcher, yet I'm here, holding my first certificate proudly. KEEP LEARNING anyone who is reading this.

The course offers a great overview of deep learning and neural networks. Andrew did a fantastic job in clarifying the taught materials such as forward and backward propagation. Thanks to all those who put together this formidable course. i will work hard to take as well the remaining courses of this specialization in machine learning.

Andrew gives a great detail in how to implement NN from scratch. Regardless your background is academic or pure industrial, you will get both the intuition and detail formulation of the material.

Please spend some time to view the optional videos, they are really important . If you would like to widen your knowledge in the field fast

Fantastic introduction course to neural networks, even if you already have a little experience. Andrew Ng is an excellent guide for this topic and presents everything in a comprehensible way. The programming exercises strike a perfect balance between guiding the learners and allowing the learners enough freedom to try their own code.

A very good overview of the basics of neural networks. The computer exercises give some practice in using the technology without being too difficult or time consuming. Ng is outstandingly good at explaining the concepts involved and in breaking down the explanations into clear, understandable steps. Overall, an outstanding course.

El curso está muy bien está muy bien estructurado y el profesor explica todos los conceptos de una forma clara y comprensible, haciendo hincapié en los aspectos fundamentales. Tiene más carga matemática de lo que había pensado en un principio pero se sigue fácilmente, los conocimientos necesarios de cálculo y álgebra son muy básicos.

What we ML enthusiast student have question in common is how to implement neural networks and deep learning model and this course will walk you through this question along with complete understanding of the steps. felling more confident now as I have completed this course. Thanks to sir Andrew NG and team for their wonderful work.

This course is very significant and you can find this course on the CV of every specialist out there. The kind of understanding given to the students is surreal. I have done and seen a lot of courses but this one stands out !! Never thought backprop could be explained at such a level. Kudus to Andrew Sir for the amazing explanations.

Easy to understand and I think knowledge about calculus is significantly important to learning neural networks if you have some basic calculus would be very good and make you a lot easier to understand. however, code and example in an assignment is very clear and easy to understand the concept even if you don't familiar with calculus

I was taking this course after the other course on machine learning by Andrew Ng. This course shared most of materials with that course, except the fact this course used python, while the other one used matlab. However, it was still worth it as both courses has different problems in assignment. Also, this course was better organized.

Amazing course! Thank you Andrew you are amazing teacher ! I had little knowledge of it but now I feel much more confident! it has helped me to understand deep neural network and deep learning fundamentals. I want to take up next sessions also, I hope in the future perhaps keras will included as its simpler compared to tensorflow .

Great and challenging course. I learned a lot. There were parts, though, that could be improved - especially in the coding assignments, where outputs do not (are not, apparently, expected to) match "expected outputs" - like first assignment of Week 4. Forum is helpful but there's room for improvement. All in all, though, very useful.