Chevron Left
Analyze Datasets and Train ML Models using AutoML に戻る による Analyze Datasets and Train ML Models using AutoML の受講者のレビューおよびフィードバック



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....



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.


Analyze Datasets and Train ML Models using AutoML: 26 - 50 / 50 レビュー

by Sam B C


I cannot proceed with the lab. It says I have reached the total lab usage time

by Tenzin T


Great! Highly recommended for emerging data scientist who are looking to gain practical knowledge on AWS.

by Yin Q


The course is well organized and the lab is easy to follow. Thanks for making this course available!

by Pitabas M


Gives a very good and quick introduction to the different features available in AWS.

by Shankar K V


Amazing course to learn about various data science concepts and AWS tools

by Pratik K


G​ood overview of general Data Science concepts and AWS Sagemaker.

by Marry C C


Great courser to learn advance machine learning pipelines in AWS!

by karthik v


This course really helped me understand about AWS services

by Alcebiades A B F


Good contents if you study de jupyter notebooks

by Flavio F


A​mazing. Best course regarding aws sagemaker.

by Viplove J G


It is a good course but i want to unenroll!

by Himasha J


great content , learn new aws services

by Gourav R


great course ,real practical knowledge

by Daniel E


Great introduction and review

by Sebastian K


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 Behnam H


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


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

by Priyabrat K B


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

by Diego M


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

by Abdallah H


good course but need more chalenges

by Sanjay C


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


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


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

by Touko H


P​aid advertisement. I paid.

by Daniele V


​Problem with graded external tool