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學生對 提供的 Analyze Datasets and Train ML Models using AutoML 的評價和反饋

262 個評分
63 條評論


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.



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


51 - Analyze Datasets and Train ML Models using AutoML 的 69 個評論(共 69 個)

創建者 Daniel E


Great introduction and review

創建者 Harsh


R​eally useful

創建者 Jonathan O


G​reat course

創建者 Brayan


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?

創建者 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.

創建者 Yue H


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.

創建者 jekasm19


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.

創建者 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?

創建者 José M F D


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

創建者 Mausumi M


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

創建者 Priyabrat K B


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

創建者 Diego M


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

創建者 Abdallah H


good course but need more chalenges

創建者 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.

創建者 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.

創建者 Lucas W d C S P


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

創建者 Kenneth N


Not a well structured course

創建者 Touko H


P​aid advertisement. I paid.

創建者 Daniele V


​Problem with graded external tool