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.
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IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
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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 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.
Retrieving Data, Exploratory Data Analysis, and Feature Engineering
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, and how to conduct exploratory analysis to visually confirm it is ready for machine learning modeling.
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.
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EXPLORATORY DATA ANALYSIS FOR MACHINE LEARNING의 최상위 리뷰
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.
Good introduction. The time estimates to complete assignments are off. Need a lot more material and direction for assignments to aid learning.
if you really make the exercises and the final assignment the course is really contributes to your better understand of Data Analysis
It is a really insightful and interactive learning experience. Furthermore, the trainers and coaches were very knowledgable.
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