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Learner Reviews & Feedback for Analyze Datasets and Train ML Models using AutoML by DeepLearning.AI

271 ratings

About the Course

In the first course of the Practical Data Science Specialization, you will learn foundational concepts for exploratory data analysis (EDA), automated machine learning (AutoML), and text classification algorithms. With Amazon SageMaker Clarify and Amazon SageMaker Data Wrangler, you will 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. You will then perform automated machine learning (AutoML) to automatically train, tune, and deploy the best text-classification algorithm for the given dataset using Amazon SageMaker Autopilot. Next, you will work with Amazon SageMaker BlazingText, a highly optimized and scalable implementation of the popular FastText algorithm, to train a text classifier with very little code. Practical data science is geared towards handling massive datasets that do not fit in your local hardware and could originate from multiple sources. One of the biggest benefits of developing and running data science projects in the cloud is the agility and elasticity that the cloud offers to scale up and out at a minimum cost. The Practical Data Science Specialization helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages and want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud....

Top reviews


Nov 8, 2021

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.


Jul 7, 2021

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

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51 - 72 of 72 Reviews for Analyze Datasets and Train ML Models using AutoML

By Daniel E

Oct 1, 2021

Great introduction and review

By Harsh

Jun 25, 2022

R​eally useful

By Jonathan O

Apr 8, 2022

G​reat course

By Asanka W

Jul 9, 2022

Great Course

By Brayan

Mar 16, 2022

M​y feelings are that this is a nice course but I'm a bit mixed on the assignments side. The notebooks were written almost entirely already and I had to simply write down the name of some variables. Is it really this easy to train and deploy ML models using the AutoML tools in SageMaker? Or were the notebooks too easy?

By Davi S B

Aug 8, 2022

Very practical indeed, and the possibility of runing form within AWS is great. The only thing I think was lacking was actually have practices where the student has to do some more. Most of the exercises were just placing the name of a variable.

By Sebastian K

Aug 17, 2021

Overall great course. Presentation by the instructors was very well done. The labs were a bit too easy, though. Exercises usually only consisted of copying and pasting a missing value from A to B.

By Yue H

Feb 8, 2022

Very useful content and helpful labs. Labs sessons expired in 2 hours and no work could be saved which is frustrating, make sure to submit work ASAP before diving into the detailed content.

By jekasm19

Feb 8, 2022

V​ery informative and provides a good runthrough of the technology and concepts. However, projects don't leave room for students to experiment with the technology for themselves.

By Behnam H

Jul 4, 2021

Great course! T​he only thing that's not specified is the cost of the tools we learn how to use. Is SageMaker free, or is there a cost?

By José M F D

Oct 6, 2021

It's good in general, but I would have liked some explanation in the style of the code walkthrough.

By Mausumi M

May 2, 2022

Week 3 lab gave me hard time. Otherwise the course is great. Lectures are short and I like that.

By Priyabrat K B

Oct 15, 2021

Good course but my doubts are not getting resolved even if i post in deeplearning community.

By Diego M

Nov 20, 2021

It is difficult to understand completely lab exercises . Very Nice course!!

By Abdallah H

Jul 17, 2021

good course but need more chalenges

By Luka

Jul 13, 2022

This course gives a high-level overview of how various aspects of data science (primarily, data exploration and AutoML) can be done by using AWS SageMaker. The labs work well from the implementation standpoint and the examples are very relevant. What could be improved: 1. delivering the content in a bit more engaging manner, 2. adding more material meat instead of glancing over the topics and mostly requiring us to change variable names to pass the course labs, without ensuring that we get deeper understanding of the APIs and concepts.

By Sanjay C

Jan 17, 2022

I was a little disappointed in the courses in this specialization - the issue is that a large part of the coding was already done. In order for this course to be an "advanced" level course, the students should be asked to write their own SQL/pandas/python code for database access and data processing.

By Michalis F

Sep 14, 2021

The course was a bit quick and the exercises are trivial to complete. Last week's context was on my opinion more useful .It would have been nice to see how to use our own scripts.

By Lucas W d C S P

Aug 29, 2021

A few interesting new tools but the course in general was very basic, and the exercises very easy

By Kenneth N

Jun 27, 2022

Not a well structured course

By Touko H

Nov 23, 2021

P​aid advertisement. I paid.

By Daniele V

Dec 14, 2021

​Problem with graded external tool