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Free Course
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Channel 9
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2-3 Hours
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Assessment
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Certification
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Academic - Third Level - Level 1
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50 Pts
Data Analytics - Introduction to Machine Learning
Join 6,517 other students
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Description
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Outcome
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Certification
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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.
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All Alison courses are free to study. To successfully complete a course you must score 80% or higher in each course assessments. Upon successful completion of a course, you can choose to make your achievement formal by purchasing an official Alison Diploma, Certificate or PDF.
Having an official Alison document is a great way to celebrate and share your success. It is:- Ideal to include with CVs, job applications and portfolios
- A way to show your ability to learn and achieve high results
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Modules List( 3 )
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Data Analytics - Introduction to Machine Learning
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Module
1 Introduction to Machine Learning-
In this module you will be introduced to machine learning. You will learn about supervised and unsupervised learning and the difference between them. You will also learn about regularization and linear regression.
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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
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Module
2 Introduction to Regression-
In this module you will learn about different types of regression. You will learn what confidence level is and how to use both Excel and Matlab for simple and multiple regression. You will also learn about the K-NN approach in data analytics.
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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
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END OF COURSE ASSESSMENT
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Module
3 Data Analytics - Introduction to Machine Learning Assessment-
You must score 80% or more to pass this assessment.
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Data Analytics - Introduction to Machine Learning Assessment
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