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學生對 IBM 提供的 Exploratory Data Analysis for Machine Learning 的評價和反饋

4.6
573 個評分
137 條評論

課程概述

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing. By the end of this course you should be able to: Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud  Describe and use common feature selection and feature engineering techniques Handle categorical and ordinal features, as well as missing values Use a variety of techniques for detecting and dealing with outliers Articulate why feature scaling is important and use a variety of scaling techniques   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Machine Learning and Artificial Intelligence in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics....

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LS
2021年4月16日

Excelente como primera iniciativa en el mundo de Coursera empezar IBM. Claras las explicaciones de todos los videos. Muy buenos notebooks para el seguimiento de los temas aprendidos. Excelente!

AE
2021年9月26日

Very detailed course of Exploratory Data Analysis for Machine learning. Ready to take the next step in data science or Machine learning, this is great course for taking you to the next level.

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101 - Exploratory Data Analysis for Machine Learning 的 125 個評論(共 139 個)

創建者 ABHIJIT B

2021年1月25日

Very good

創建者 Vitor R

2020年11月24日

Awesome!!

創建者 Rohit K

2021年1月14日

good one

創建者 Hariom K

2022年1月18日

Thanks

創建者 Sathisha

2021年8月3日

got it

創建者 William G G B

2022年1月17日

G​ood

創建者 Ali A A

2020年10月26日

great

創建者 Sabina

2021年10月19日

Good

創建者 Miguel B D S N

2021年1月26日

Nice

創建者 Emanuele F

2021年9月26日

The course touches all the topics that are of interest for the a Machine Learning pratictioneer. I've found the course sometimes oversimplified, that paradoxically made it harder to grasp some concepts, expecially the topics of the Week 2. Overall I've found It to be a good course because at least it gives you the path to follow from where you can study on your own to go deeper in the topics you are interested.

Note: I would suggest to edit the notebooks. It is not a good idea to have the solutions in the same notebook where you should do an exericise, because it makes also the video lectures that came after pretty useless. I suggest to separate the exercises from the solutions, and to put the solutions in the video lectures so you must follow them with some focus to understand what the solution was. Furhtermore i would review the notebooks. Some of them were different from the ones presented in the video lectures which made it a little bit confusing to follow.

創建者 Alexander S

2021年4月25日

The quality of this course is very good. It helped me to get a basic understanding of exploratory data analysis. Whereas the first weeks topic was more or less early for me, the seconds weeks topic about statistics was more challenging and I also had to do some own research to deepen the contents discussed in the lectures.

創建者 Anna R

2021年11月15日

I really liked this course, has been extremely useful for me as a starting point for next IBM courses. One suggestion to improve - some concepts are covered a bit superficially, in my view, e.g. Hypothesis Testing. Maybe going a bit more into theory would help.

創建者 jake t

2021年1月5日

The information was good though basic. I thought the info on hypothesis testing and probability was probably not necessary for an ML course where this should be assumed. The teacher was clearly reading off a script which was at times not so engaging.

創建者 Arnav G

2021年5月24日

It is fairly difficult for a beginner - although the level is intermediate for this course and there are a few prerequisites, somehow I still feel that a lot is pending to be explained, esp. in the DEMO/LAB exercises

創建者 Ghanem A

2021年9月30日

Excellent content and examples. Would be great if another example for hypothesis testing is added to demonstrate this concept with a typical ML dataset (maybe use one of the previous datasets used during the course)

創建者 Erick A

2021年6月28日

Great instructions, wonderful demos and insightful comments on the results. The only part that I did not find well explained is the part on hypothesis testing. Some details could be added on t-test and z-test.

創建者 Ignacio A S B

2022年1月21日

It lacks a deeper view of the topics and applications on programming for a real world Exploratory Data Analysis (EDA), but gives the basic tools and understanding to introduce you to EDA.

創建者 Alexandros A

2021年12月14日

The first week of this course was very informative and with a lot of examples. Although, the second week was difficult to understand, the concepts nad the examples were not clear.

創建者 Aditya M

2020年12月18日

Good introduction. The time estimates to complete assignments are off.

Need a lot more material and direction for assignments to aid learning.

創建者 Dany D C

2021年5月2日

I know some basic statistics knowledge is required, but sometimes the analysis story is unconnected, and sometimes make the story confusing.

創建者 JORGE M B

2021年12月10日

The course is good. What it lacks to get the 5 stars is to be able to download the slides of the classes or to have a documentation.

創建者 Arunav C

2020年10月1日

It is a really insightful and interactive learning experience. Furthermore, the trainers and coaches were very knowledgable.

創建者 Mahmudul F A A

2020年11月6日

In week 2, the lessons were a bit in rush and it would be better to have a bit more detailed discussion.

創建者 Olivier F

2021年10月7日

G​ood introduction and Exploratory Data Analysis course.

創建者 CHIARA B

2021年9月18日

A good background in math and some python is needed.