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.
Exploratory Data Analysis for Machine Learning
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Exploratory Data Analysis for Machine Learning
This course is part of multiple programs.
Instructors: Joseph Santarcangelo
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Gain insight into a topic and learn the fundamentals.
4.6
(2,300 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 14 hours
Learn at your own pace
95%
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Skills you'll gain
Details to know
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Assessments
12 assignments
Taught in English
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There are 5 modules in this course
Artificial Intelligence is not new, but it is new in a sense that it is easier than ever to get started using Machine Learning in business settings. In this module, we will go over a quick introduction to AI and Machine Learning and we will visit a brief history of the modern AI. We will also explore some of the current applications of AI and Machine Learning for you, to think about how you want to leverage them in your day to day business practice or personal projects.
What's included
10 videos2 readings3 assignments1 discussion prompt
Good data is the fuel that powers Machine Learning and Artificial Intelligence. In this module, you will learn how to retrieve data from different sources, how to clean it to ensure its quality.
What's included
7 videos3 readings3 assignments3 app items
In this module you will learn how to conduct exploratory analysis to visually confirm it is ready for machine learning modeling by feature engineering and transformations.
What's included
15 videos3 readings3 assignments4 app items
Inferential statistics and hypothesis testing are two types of data analysis often overlooked at early stages of analyzing your data. They can give you quick insights about the quality of your data. They also help you confirm business intuition and help you prescribe what to analyze next using Machine Learning. This module looks at useful definitions and simple examples that will help you get started creating hypothesis around your business problem and how to test them.
What's included
16 videos2 readings3 assignments2 app items1 discussion prompt
In this assignment, you will apply the skills learned throughout the course to analyze a dataset of your choice, either from the course materials or an external source. You will perform data cleaning, feature engineering, exploratory data visualization, and hypothesis testing to derive meaningful insights. Upon completion, your work will be evaluated automatically by an AI grading tool.
What's included
4 readings1 app item
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4.6
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Showing 3 of 2300
BD
5
Reviewed on Apr 23, 2024
The course includes hands-on exercises that allows us to apply the learned EDA techniques to real-world data. This practical approach helps solidify my understanding.
AP
5
Reviewed on Feb 25, 2023
This course was amazing. I always assumed that EDA was the challenging part of ML, But in this course I found it so cool. can't wait for the next course.
AS
5
Reviewed on Aug 15, 2021
IBM courses are most valuable courses, quite a lot of learning happens here. I recommend students when it is time to chose a Brand IBM can be considered in top 5 List. Happy learning.
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