Analyse machine learning and common algorithms

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Reference no: EM133799324 , Length: word count:600

Artificial Intelligence and Machine Learning in IT

Assessment - The Algorithms and Applications of AI/ML

Your Task
Your first assessment requires you to apply the concepts learnt during the first four weeks of the subject, as well as researching the supplied references in this document.

Assessment Description
The slide deck is an individual PowerPoint presentation explaining some concepts learnt related to AI/ML. You are expected to research the supplied references and review workshop slides to develop a good understanding of the different types of ML algorithms and the applications, especially how they are going to support the business.

Learning outcome 1: Evaluate artificial intelligence algorithms in information technology
Learning outcome 2: Analyse machine learning and common algorithms

Assessment Instructions PowerPoint Structure
You are required to create an individual slide deck with dot points and illustrations as per the following:

Slide 1: Cover page
The title of the assessment, your name, student ID and date.

Slide 2 - 3 Industry 4.0
Characteristics of industry 4.0
How industry 4.0 differs from industry 3.0
Reading:

Slide 4 - 5 Fundamentals of AI and ML
Briefly describe what AI and ML are respectively with an example for each
The relationship between AI and ML

Slide 6 - 10 Types of Machine Learning
Supervised learning
What the common algorithms are
Provide an example of how supervised learning is being used in business
Unsupervised learning
What the common algorithms are
Provide an example of how unsupervised learning is being used in business
Major difference between Supervised learning and Unsupervised learning

Slide 11 - 12 Python Libraries in Machine Learning
Research and contrast three Python libraries that are used in Machine Learning

Slide 13 Reference
Add your references (at least 5 Get Your Assignments Done Now!) in this slide using any professional and consistent styling.

Reference no: EM133799324

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