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.
Offered By
IBMAbout this Course
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Intermediate Level
Approx. 14 hours to complete
English
Subtitles: English
Skills you will gain
- Artificial Intelligence (AI)
- Machine Learning
- Feature Engineering
- Statistical Hypothesis Testing
- Exploratory Data Analysis
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level
Approx. 14 hours to complete
English
Subtitles: English
Offered by
IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Syllabus - What you will learn from this course
2 hours to complete
A Brief History of Modern AI and its Applications
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.
2 hours to complete
10 videos (Total 57 min), 2 readings, 3 quizzes
3 hours to complete
Retrieving and Cleaning Data
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.
3 hours to complete
7 videos (Total 43 min), 3 readings, 6 quizzes
5 hours to complete
Exploratory Data Analysis and Feature Engineering
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.
5 hours to complete
15 videos (Total 104 min), 3 readings, 7 quizzes
3 hours to complete
Inferential Statistics and Hypothesis Testing
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.
3 hours to complete
16 videos (Total 88 min), 2 readings, 5 quizzes
Reviews
- 5 stars71.51%
- 4 stars20.57%
- 3 stars4.51%
- 2 stars2.38%
- 1 star1%
TOP REVIEWS FROM EXPLORATORY DATA ANALYSIS FOR MACHINE LEARNING
by PZApr 6, 2021
Very well curated course. Walks through all the topics in detail. Would be better if the professor had a little bit higher voice.
by DSNov 30, 2020
The only reason that I do not give it 5 stars is because the website of coursera is not good enough to handle the peer review assignments at the end of the course.
by NWDec 7, 2020
Recommended course for those wanting to advance their understanding about Exploratory Data Analysis for Machine Learning.
by DCMay 1, 2021
I know some basic statistics knowledge is required, but sometimes the analysis story is unconnected, and sometimes make the story confusing.
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