糖心vlog官网观看

Chevron Left
Back to Exploratory Data Analysis for Machine Learning

Learner Reviews & Feedback for Exploratory Data Analysis for Machine Learning by IBM

4.6
stars
2,267 ratings

About the Course

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聽 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 聽 Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience聽 with Machine Learning and Artificial Intelligence in a business setting. 聽 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....

Top reviews

HV

Nov 11, 2024

With my background on probability and statistics, I think this is a good course, where it can help me apply what i have learned. Not recommend for any one who hasn't taken a statistics course before.

AE

Sep 27, 2021

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.

Filter by:

301 - 325 of 461 Reviews for Exploratory Data Analysis for Machine Learning

By lucas a

鈥�

Aug 16, 2023

gooood

By Shruti Y

鈥�

Feb 28, 2023

useful

By Hariom K

鈥�

Jan 18, 2022

Thanks

By Sathisha

鈥�

Aug 3, 2021

got it

By Tamajit N

鈥�

Nov 8, 2024

Great

By 21_085 R E S

鈥�

Jun 2, 2024

Great

By Mohsen M

鈥�

Feb 3, 2024

10/10

By DAVID R

鈥�

Feb 28, 2022

goods

By William G G B

鈥�

Jan 18, 2022

Good

By Ali A A

鈥�

Oct 27, 2020

great

By Sanchi C

鈥�

Jun 21, 2025

good

By Neema R

鈥�

Jun 9, 2025

good

By Ishan d

鈥�

Jun 7, 2025

good

By priyankar n

鈥�

Mar 21, 2025

Nice

By Khunakhan A

鈥�

Feb 7, 2025

imba

By Anurag T

鈥�

Feb 5, 2025

good

By Krishna K K

鈥�

Dec 21, 2024

good

By 袛懈邪褋 袗

鈥�

Dec 10, 2024

good

By SOMYA P

鈥�

Nov 25, 2024

good

By Gandham H

鈥�

Nov 13, 2024

good

By Ayuska S

鈥�

Oct 31, 2024

good

By Rudra P J

鈥�

Oct 9, 2024

Nice

By Sahil

鈥�

Sep 15, 2024

good

By shashank s

鈥�

Sep 7, 2024

good

By GUDITI J B

鈥�

Feb 29, 2024

Nice