Should have been more challenging

This course is rated advanced... But the labs are very very basic where you only have to replace a variable name in some key places. ALL the labs are like that. This focus a lot on automl stuff where you don't really understand what you are doing... Nothing really on how to interpret the results from those automl reports and models. The dataset they use in labs are huge, which makes you wait 10+ min for training. We could have understood the principles by using smaller datasets and not wait. I would not recommend this course for any serious ML practioner.

The videos and links were good. The labs were a bit too easy, mostly about copying variable names from the previous section.

The contents were okay, but presentation was awful. Completely read off a script it lacked any depth I am used to from Andrew's lectures. Also, the exercises were WAY too easy, you do not need any DS expertise to finish them - other than what was deemed a requirement in the opening text of the course.

U​seless course, just an advertisemeng for aws sagemaker. In fact I am the stupid one who didn't realize it before enroling.

H​aving working experience with Sagemaker is valuable course setting is nice and material is up to date I was looking for getting some hands on experience working with the python and notebook. Also I gained some experience with Blazingtext algorithm and read about the material provided.

In my opinion you do not learn any code experience by filling in some pathways. I liked to acquire more coding skills.

The course was not good. The videos were shallow, the teachers read without looking at the camera, the shapley values were not shown but instead RF feature importance was used, and the assignments were about learning Amazon tools, not anything new on how to do a real deployment and understand the algorithms.

Liked the course. It was awesome to learn data science with AWS

really good course, direct to the point with aws. I really recommend create a account and review yourself all learning.

Detailed code walk through explaining the code would have been helpful similar how it was done in Tensorflow In Practice Specalization

It looks like a paid AWS ad. The exercises are just copying and pasting some variables, you can complete them without understanding of what you are doing. And some times they are not even relevant to the topic in question. Super shallow. Disappointing.

This course was very informative and practical. I learned foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. With Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler, I am able to analyze a dataset for statistical bias, transform the dataset into machine-readable features, and select the most important features to train a multi-class text classifier. Using Amazon SageMaker Autopilot, I then used automated machine learning (AutoML) to automatically train, tune, and deploy the best text classification algorithm for the given dataset. Thanks to Coursera, DeepLearning.AI, and Amazon for this great opportunity.

This course provided the missing link in learning aspects of Sagemaker APIs I was having trouble figuring out on my own. The material is immediately applicable to work and more. Very practical just as advertised.

F​antastic Course , explores multiple AWS Services and toolkits for Data science and AI ML Solutions.

Nice information in the course. However, the practice notebooks are really not assisting in active learning of what we've applied. The use of sagemaker in the notebooks are already filled in for creating S3 buckets. Same goes for the autoML and deployment. If the notebooks would be more active where the user would write the code more themselves with less excersises, I would have had a much nicer learning experience.

The beginning of the course was a bit slow-paced and look more like a marketing campagin for AWS product : AWS Glue, AWS Athena, etc.. but I like a lot the part on AWS SageMaker Autopilot for auto-ML and the implementation of BlazingText for NLP and Sentiment Analysis. It finishes really strong.

The course was useful for learning the basics of Sagemaker Studio. I would've preferred the graded labs to require more work than just filling in a few blank variable names, but this kind of thing seems to be standard for these kinds of courses.

Coding exercises are a bit too structured, there isn't as much learning as I would have liked. That said, having the notebooks for reference at work is quite useful. Good introduction.

A good introductory course with a well thought out structure. Quite easy, but gives you a good grasp on the demonstrated Sagemaker features and it will definitely prepare you for digging deeper, having a conceptual understanding of it. Also, as a senior software engineer with years of deep experience in AWS, I really appreciate creating all resources via libraries/CLI instead of clicking around in the console - thumbs up!

Seriously I never expected to learn so many new methods, I am fascinated with the resources and the teaching techniques. Delivering information and great programmatic explanation.

Very nice course, nice presentations. The difficulty level could have been a bit higher but all in all is a good course to get hands-on experience using data science tools on AWS.

Excellent introductory course for Aws sagemaker. Justifies the specialization title as it is indeed practical oriented. Labs are of good quality as well.

As always, I am overwhelmed with the course structure. Simple to learn and had enough practice to get started with cloud services.

This course introduced me to the Amazon Sage Maker Studio and helped me in understanding the concept of Auto ML.