Free Online Data Analytics - Machine Learning Course | Alison

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Data Analytics - Introduction to Machine Learning

Understand machine learning and its use in data analytics with this free online data analytics course.

Data Science
Free Course
Machine learning is an essential part of data analytics. Still, not many people fully understand what machine learning is, or how it works. This free course will get you up-to-speed with the most important machine learning topics today. Aside from teaching you about automation, the course also covers supervised and unsupervised learning and will introduce you to important computing methods to help you find hidden information within your data.





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This online data analytics course will get you up-to-speed with supervised learning and data, unsupervised learning and data, and reinforced learning. You will also gain a solid understanding of linear regression and confidence levels. You will be shown how to use Excel to perform a Multiple Regression, and will be guided through the most important formulas in a clear and step-by-step manner so that there is no room for confusion or error.

Next you will learn the concepts of regularization and how to avoid over-fitting data analytics programs. You will then be introduced to subset selections and will be shown how to distinguish between R&sup2 and adjustment R&sup2. By the end of the course, you will have a clear understanding of the K-NN approach in data analytics and when this approach should be used.

If you are want to be a professional working in the areas of data analytics or data science, or if you would simply like to learn more about the methods used in machine learning, don't pass up this data analytics course. The course will also be useful for students who are interested in computer science and would like to learn more about data analytics. So, check out the course now and get ahead of your peers!

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Learning Outcomes

Having completed this course you will be able to: - Define the difference between supervised learning, unsupervised learning, and reinforced learning. - Explain what linear regression is. - Describe when regularization can be used. - Distinguish between supervised and unsupervised data. - Define what confidence level is. - Explain how to use Excel to perform a Multiple Regression. - Explain subset selections. - Distinguish between R² and adjustment R². - Describe the K-NN approach.


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