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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.
Description
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 R² and adjustment R². 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!
Start Course NowModules
Module 1: Introduction to Machine Learning
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Learning Outcomes
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Introduction to Machine Learning
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Supervised Learning
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Unsupervised Learning
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Lesson Summary
Module 2: Introduction to Regression
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Learning Outcomes
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Ordinary Least Squares Regression
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Simple and Multiple Regression in Excel and Matlab
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Regularization/Coefficients Shrinkage
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Data Modelling and Algorithmic Modelling Approaches
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Lesson Summary
Module 3: Data Analytics - Introduction to Machine Learning Assessment
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.
Certification
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:
Ideal for sharing with potential employers - include it in your CV, professional social media profiles and job applications
An indication of your commitment to continuously learn, upskill and achieve high results
An incentive for you to continue empowering yourself through lifelong learning
Alison offers 3 types of Certification for completed Certificate courses:
Digital Certificate - a downloadable Certificate in PDF format, immediately available to you when you complete your purchase
Certificate - a physical version of your officially branded and security-marked Certificate, posted to you with FREE shipping
Framed Certificate - a physical version of your officially branded and security-marked Certificate in a stylish frame, posted to you with FREE shipping
All Certification is available to purchase through the Alison Shop. For more information on purchasing Alison Certification, please visit our faqs. If you decide not to purchase your Alison Certification, you can still demonstrate your achievement by sharing your Learner Record or Learner Achievement Verification, both of which are accessible from your Dashboard. For more details on our Certification pricing, please visit our Pricing Page.
Careers
This Course has been revised!
For a more enjoyable learning experience, we recommend that you study the mobile-friendly republished version of this course.
Take me to revised course.
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