Comentários e feedback de alunos de Introduction to Machine Learning da instituição Universidade Duke

Very Good course explaining the theoretical concepts related to deep learning . Thank you

Excellent course. Concepts such as gradient descent and convolutions as they pertain to neural networks are explained without going into the mathematical details but, in my opinion, are explained more intuitively and better, as compared to most other courses. The course does include some ungraded Jupyter notebooks exemplifying key elements of deep learning networks. Highly recommended to 'cement' understanding of neural networks.

It was great to touch new professional area and to understand its fundamentals. The course gives a broad view on machine learning, so I think now I really understand, what the machine learning is and how to use it in my work and even my political investigations.

I like this introductory course, very good one to start to learn machine learning. I will definitely continue studying and re-watch the videos.

This was a really great course for understanding the basics of machine learning through a lot of simple but relevant, real world examples.

A very fine tuned Course,used as a warm up course for deep learning,highly recommended

Great course ! Pr Carin is clear enough to make you understand complex concepts like LSTM. The Math, calculus, algenra and prob are not too difficult. I enjoyed to follow this course ! To conclude a good introduction to ML to make you go deeper into the subject

GOOD COURSE

Great course for beginners, did a lot to fill in the gaps in my knowledge. There could be a little more help with the actual coding parts of the project, the work done in ipython notebook is largely self-taught.

Much weaker than Stanford offerings. Strange buildup of topics for a breezy, but not particular accurate understanding. For example: multiple layers of a neural network is introduced before multiple category classification. Transfer learning is introduced incorrectly. The matrix representation of multiple features of an example with multiple examples is introduced very late in the course. The instructor is conscientious and seemingly knows the material despite using non-standard terminology. One wonders if he is primarily a teacher/researcher and rarely a practitioner. One wonders if Duke is a leader in machine learning research.

Great Course!

A very comprehensive and clear introduction to the field of ML.

I have a background in pathology and I wanted to understand how machine learning works so that I can take an active part in the changes within my field and understand what is happening. This course was an amazing experience of learning, for someone like me with no background in calculus or linear algebra.

very good for getting started.

Easy to understand about machine learning

Excellent introduction in to machine learning and paced ideally to keep the interest throughout the course. Ignites interest to the field.

Very Interesting Course

Excelente

Simply Brilliant

Very good introductory course, I highly recommend it to anyone looking to get a flavour of the methods behind the recent advances in AI without going into super-technical details.

Amazing course

I liked the pace and the tensor flow applications. This should be upgraded to TF 2.0 at some point. Also, I would've appreciated some GAN material.

It would be great to have more small practical exercises as it always reinforces the theory explained

Good way to start ML journe

The course is a good balance between learning key concepts and doing coding, the coding being optional. The phrasing of quiz questions and answers were sometimes confusing.