Free Certification Course Title: Master Data Mining in Data Science & Machine Learning
Learn about Data Mining Standard Processes, Survival Analysis, Clustering Analysis, Various algorithms and much more.
What you’ll learn:
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Get started with Data Mining.
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Learn about different Data Mining Standard Processes.
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Learn the concept of Survival Analysis.
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Learn about the concept of Cox Hazards Regression.
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Learn about Clustering Analysis.
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Learn about the Dimensionality reduction.
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Learn about the concept of Association Rule Learning.
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Learn about the Predictive Modelling.
Requirements:
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Basic knowledge is required in Statistics.
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Basic knowledge required for Python.
Who this course is for:
- People who are quite beginners.
- Anyone who is curious to learn Data Mining.
- Anyone who is interested in learning Data Mining algorithms.
- Data Scientists.
- Machine learning experts.
Description
If you are looking to build strong foundations and understand advanced Data Mining techniques using Industry-standard Machine Learning models and algorithms then this is the perfect course is for you. We have covered everything you need about Data Mining and its processes, Machine Learning Models, and how to implement them in the real world.
Data mining means mining the data. It is defined as finding hidden insights(information) from the database and extract patterns from the data.
Data mining is an automated process that consists of searching large datasets for patterns humans might not spot.
In this course, you will get advanced knowledge on Data Mining.
This course begins by providing you the complete knowledge about the introduction of Data Mining.
This course is a complete package for everyone wanting to pursue a career in data mining.
This course includes:
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5.5 hours on-demand video
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2 articles
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Full lifetime access
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Access on mobile and TV
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Certificate of completion
How to Subscribe for Master Data Mining in Data Science & Machine Learning?
- Sign Up on Udemy.com
- Subscribe Here(Master Data Mining in Data Science & Machine Learning): Click Here
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Table of Contents
