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Exploratory Data Analysis for Machine Learning(으)로 돌아가기

IBM의 Exploratory Data Analysis for Machine Learning 학습자 리뷰 및 피드백

4.6
별점
746개의 평가
171개의 리뷰

강좌 소개

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....

최상위 리뷰

AE

2021년 9월 26일

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.

ML

2021년 9월 21일

Excellent, very detailed. However, if the lessons can be expand for hypothesis testing and some of their common test like T test, Anova 1 and 2 way, chi square,..it would be better further.

필터링 기준:

Exploratory Data Analysis for Machine Learning의 175개 리뷰 중 151~175

교육 기관: Mahmudul F A A

2020년 11월 6일

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

교육 기관: Medha J

2022년 3월 14일

Very Nice course , will teach you in detail all the techniques of EDA with practical code.

교육 기관: Aravind S

2022년 4월 11일

Was able to learn and practice many topics in this course. Very useful for Data Analysis.

교육 기관: Sebastian N

2022년 5월 16일

Good instructor, good knowledge level, minor mistakes in some of the notebooks provided.

교육 기관: Joseph F

2022년 5월 2일

Good introduction. Quiz questions mostly on terminology and not understanding.

교육 기관: Roberta D

2022년 4월 13일

Very interesting course, good for getting ideas to deepen the topic!

교육 기관: Daren L P

2022년 6월 27일

I​ enjoyed the course, the example code/labs were awesome

교육 기관: Olivier F

2021년 10월 7일

G​ood introduction and Exploratory Data Analysis course.

교육 기관: CHIARA B

2021년 9월 18일

A good background in math and some python is needed.

교육 기관: Miguel D

2021년 5월 12일

I wish the hypothesis part was a bit more detailed

교육 기관: DONG C

2021년 9월 9일

B​etter than other IBM ML certificate series

교육 기관: Tania L

2021년 10월 19일

Quite interesting course for beginners

교육 기관: Chandan K G

2021년 7월 19일

It was nice learning experience.

교육 기관: OMAR A H H

2020년 11월 1일

Very well structured

교육 기관: Pampa D

2022년 4월 18일

Good content.

교육 기관: Hossam G M

2021년 5월 27일

The course material should be provided to allow better absorption of the large amount of information presented. some of the topics needs to be discussed further with more examples and concept declaration especially the hypothesis testing section.

교육 기관: Gabriel Y H M

2021년 2월 25일

I liked the course content but I would like a more interactive approach that show us how to do hypothesis testing in python. The teacher just reads the courses.

교육 기관: Azmine T W

2022년 4월 16일

I think, instructor went too fast in many cases. Some topics needs to be restructured with more real life examples and interpretations.

교육 기관: Simon N

2021년 4월 19일

I do like the course in generall. But some slides, are very text heavy, which i do not prefer.

교육 기관: Busola A

2022년 3월 29일

The videos are not well explanatory enough.

교육 기관: Oleg O

2022년 3월 25일

This course is too surface. You must have a solid background in statistics and be familiar with pandas/numpy python libraries, otherwise you will spend a lot of time just to learn these libs. Also there is some basic info in lectures but assignments contain much complex and harder tasks which were not discussed in the lecture. And the tasks already have answers , so there are questions and solutions in one place, it is very weird and annoying

교육 기관: Stephen C

2022년 1월 3일

Frankly, the presenter is a poor educator and the course materials are weak. The examples are limited, some explanations verge on incorrect (description of p-values), and several of the graded test questions are ambiguous and encourage rote learning of the teacher's preference/positions, rather than testing the underlying concepts. I expect better from IBM.

교육 기관: Dimitrios T

2022년 6월 13일

Poor explanation of many concepts. Felt i the instructor was reading the material in a neutral manner and was not emphasizing on key moments. Also lack hands on opportunities and practice to help understand the concepts.

Overall seemed more like a summary of various titles and definitions.

교육 기관: Mpho M

2020년 12월 1일

Course videos are way too long.

No Jupyter support, so for the coding exercise one has to download the notebooks and either use Google Colab or locally installed Jupyter notebook.

교육 기관: Walter c

2021년 6월 14일

The course starts well. Then it goes to statistics and not so much to machine learning. The assignment is not so geared towards machine learning.