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Data Analytics - Introduction to Machine Learning

Data Science
Free Course
Learn about machine learning and its use in data analytics.
  • Duration

    2-3 Hours
  • Assessment

    Yes
  • Certification

    Yes
  • Publisher

    Channel 9
Description Outcome Certification View course modules

Data Analytics – Introduction to Machine Learning is a course that will teach you about machine learning methods that help automate the analysis of data. These computing methods help find hidden insights and information within the data without being explicitly programmed where or what to search for within the data.

This course begins by introducing you to supervised and unsupervised learning. You will learn how to distinguish between each type of learning and how to use them to analyse data. You will also learn about linear regression and how it can be used. The course introduces concepts about regularization and how to avoid over-fitting by using regularization.

Next, you will learn about using Excel and Matlab to perform simple and multiple regression. You will learn about confidence levels and subset selections. You will learn how to distinguish between R² and adjustment R² and what they both measure. The course will finish by introducing what the K-NN approach is in data analytics and when this approach should be used.

This course will be of great interest to professionals who work in the areas of data analytics and data science and who would like to learn more about methods used in machine learning. It will also be of interest to learners who are interested in computer science and would like to learn more about how machine learning gives computers the ability to learn without being explicitly programmed.

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.

All Alison courses are free to enrol, study and complete. To successfully complete this Certificate course and become an Alison Graduate, you need to achieve 80% or higher in each course assessment. Once you have completed this Certificate course, you have the option to acquire official Certification, which is a great way to share your achievement with the world. Your Alison Certification is:

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Alison offers 3 types of Certification for completed Certificate courses:

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