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
Exploratory Data Analysis for Machine Learning
This course is part of multiple programs.
Instructors: Joseph Santarcangelo
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4.6
(2,014 reviews)
Intermediate level
Some related experience required
Flexible schedule
Approx. 14 hours
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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 optional HONORS project you will apply your skills and knowledge learned throughout the course. You can select a dataset from the ones used in this Course or any other dataset of interest and apply all of the demonstrated techniques including, Data Cleaning, Feature Engineering, Exploratory Data Visualization, and Hypothesis Testing.
What's included
1 reading1 peer review
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4.5 (662 ratings)Offered by
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4.6
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Showing 3 of 2014
KG
5
Reviewed on Nov 4, 2022
Good introduction to the workflow in EDA for ML. I appreciate the code examples that provide a useful reference to code syntax and some practice with EDA.
V
5
Reviewed on Jun 9, 2021
Very nice course which explains beautifully about data cleaning and the statistical approach and then statistic model and then it ends with the hypothesis testing.
ML
5
Reviewed on Sep 21, 2021
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.
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