Great course. Gradient descent and loss function concepts were explained well.

The videos are ok, the "Labs" are really bad. You just follow instructions with code to copy into the notebook. Of course, you can play a bit with the code, but you don't really learn how to do it yourself when the correct answer is already filled in. Would not recommend.

Great lectures and labs thanks. The first lecture block made a lot of great connections between topics and methods past and present yet get the most out of it, one ideally has recently reviewed the theory behind the tradition tools. Otherwise the first block is a bit of a drink from the firehose although one can still pick up the gist message but may not get some of the other enriching points. In any case. Great work and thanks

Today is my day Learning GCP AI platform have a fun time discovering data pre-processing with Big Queries, deploy TensorFlow notebook and play with Benchmark model. The fun time is having a chance to take a look at the Google Cloud AI platform and have a fun time with it😊 ! Thank you Google and Coursera give us the scholarship to read and have a fun time with These 🧪 🧫 labs☺️! You light 💡 my day🍀! We love you ~~✧٩(ˊωˋ*)و✧

This course builds on the previous one. Although use is still made of Google cloud, the course becomes more interesting, since the teachers provide their practical insight in the preparation of data for machine learning without focusing too much on Google. The history of machine learning is very interesting and the labs very useful in understanding the main pitfalls associated with the preprocessing step.

An excellent introduction to Machine Learning, I appreciated the explanations around the importance of having proper training, validation and testing set to build robust models, I loved the introduction to Big Query and the value of cleaning the datasets, plus all the explanations around Classification Models,Regression Models and Gradient Descent.

Thanks

Thanks to team of Google Cloud Platform for giving such dramatic and interesting course for me to acquire critically fundamental knowledge of conducting ML! It leads to all the places I've never thought of, and now I'm prepared to accept the challenge to bring ML into solving real-life problems, in the hope of making the world more sense!

Fluent flow of introduction using examples. Gives an overview of the ML process concepts and tips. Detailed concepts are only mentioned quickly, so to fully benefit from the course would probably require some prior experience in ML. Valuable insights and summary from experienced ML engineer are provided in this course.

I love the course introducing core concepts and practices in machine learning today as well as some historical development. This course feels more rigorous because some core mathematical foundations are introduced. I kind of hope there could be more theoretical explanation in more depths with some references attached.

I loved TensorFlow Playgrounds. It made so many concepts visible. I have more intuition into how the number of layers, input features and number of neutrons affect what the model can learn. This is not my first Machine Learning course, and it is helping me fill out many gaps I have in my understanding.

Thank you for such great knowledge sharing. I have really enjoyed the course and have learned a lot from it. The way the speakers explain each and every tiny detail is exceptional. This course make me a step closer to my goals and will help me in my career building as a Machine Learning Engineer.

This course is insightful. I'm new to SQL, so I couldn't understand what was going on in the lab. I still find it enjoyable, and I think I've learned a lot. Maybe I'm not able to know how to split data as good as the instructors, but I'll use the resources on GitHub to keep improving.

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

Most of time, dataset for ML would come from the structured databases and datawarehouses. This course outlines how to get random sample from BigQuery, which is useful for conventional ML scenarios. Kudos to GCP team!

Very practical, pragmatic and to the point. The labs are great! The history of ML and the section on Generalization are my favorite because instructors gave detailed explanations and precise instructions.

This is an awesome module. It will open up so much inside story of ML process which is core of the topic with such a simplicity. It greatly increases my interest into this topic and this course :)

Good course, covering all the basics about machine learning and most importantly, everything that surrounds an ml project and you need to take into account to make your ml project successful.

My favourite course in the specialisation. I think it's a great idea to use historic time-frame to explain the advances in ML and why there is so much hype around deep learning.

This course gave me a good overview of how to work with GCP for ML and also helped in covering a bit of knowledge gaps that I had when I learnt things on my own.

A great course to boost your confidence on practicing ML. It also teaches you some fresh skills like repeatable dataset partitioning techniques using just SQL.

This course is very helpful to understand the machine learning concepts of various modals, splitting of the data and even training the model for benchmark.

Excelente curso, muy recomendado para ampliar el entendimiento sobre Aprendizaje Automático, me gustó mucho haber podido usar Tensorflow Playground

I learned machine learning well with this class. Thanks to Google for making these lecture films and allowing us to learn with these lecutres.

The technical knowledge is introduced very progressively. You understand the historic evolution and practical usage of models. Great content!

The math made me pull out old calc textbooks, but very good building of where the ML process is headed in terms of getting good sample data.