Assignment 1 (Unsupervised, Supervised, and Reinforced Machine Learning algorithms):
Establish the beginning of a machine learning proposal that would recommend the use of machine learning algorithms in a practical and real-world scenario. For this opportunity, provide the background on the nature of the opportunity, give potential data to be used, and clearly articulate the desired goal and outcome. Ensure data are available for this solution because you will be modeling with it in Week 4. Potential data sources can be found at data.gov, kaggle.com, and many others. This proposal will be communicated to both a technical and nontechnical audience.
During Week 1, you will establish the foundation and shell document for your final assignment (your machine learning proposal that will be applied to a practical and real-world scenario).
The project deliverables include the following:
· Identify a machine learning opportunity
· Provide the background on the opportunity and why machine learning will benefit the scenario.
· Discuss the data to be used within the opportunity.
· Clearly articulate the desired outcome when successful machine learning is applied.
· Discuss 3 potential algorithms that could be used.
· Discuss the data assumptions for these algorithms.
· Discuss how algorithm performance will be evaluated.
· Include code examples when necessary.
The draft paper should be 10–12 pages, including empty sections. It should be formatted using APA style and include at least 2 references. This week, you will create 3–4 pages of original content.
Assignment 2 ( Unsupervised, Understanding the role of algorithms in data visualization):
During Week 2, you will extend the machine learning proposal that recommends the use of machine learning algorithms in a practical and real-world scenario. For each of the 3 identified algorithms in Week 1, provide at least 1 visualization technique to illustrate the use and benefit of visualization. Include examples as necessary to support the proposed visualizations.
Using the partially completed template you created last week, add 3–4 pages of new content. It should be formatted using APA style and include at least 2 references.
Assignment 3(Algorithms for Streaming Data):
During Week 3, you will extend the machine learning proposal to provide information and an approach for utilizing streaming data.
The project deliverables must include the following:
· Streaming data
· Discuss the differences in streaming data over static data. In this discussion, include what differs when algorithms utilize streaming data versus static data.
· Discuss how any challenges may be overcome to ensure performance and the use of the algorithm across streaming data.
· For the identified machine learning opportunity, determine if streaming data may be available, and discuss how it would be utilized.
Using the partially completed template created in Week 1 and extended in Week 2, add 3–4 pages of new content. It should be formatted using APA style and include at least 2 references.
Assignment 4 (Algorithms for Practical Use):
During Week 4, you will extend the machine learning proposal to include a prototyped algorithm for the proposed solution.
The project deliverables for Week 4 must include the following:
· A flowchart that is relevant to the steps of the machine learning process, from data ingest through communicating the results or taking actions based on the findings of the prototyped solution.
· A discussion of the data characteristics, chosen algorithm, visualization(s) for the algorithm, and code for the algorithm(s).
· Any code or screenshots, as well as the visualization outputs for the prototype.
Using the partially completed template created in Week 1 and extended in Weeks 2 and 3, add 3–4 pages of new content. It should be formatted using APA style and include at least 2 references.
Assignment 5(Demonstration and Communication of Algorithm use):
During Week 5, you will extend the machine learning proposal to include an executive summary. Additionally, you will create a summary presentation of 8–10 content slides that communicate the role of machine learning to a nontechnical audience.
The project deliverables for Week 5 include the following:
· 1-page executive summary of the machine learning proposal. Your total machine learning proposal should be 13–16 pages long.
· Summary presentation
· Include 8–10 content slides in addition to the title and reference slide
· Summarize the course topics within the 8–10 content slides for the specific proposal researched; the slides should include the following:
· Unsupervised, supervised, and reinforced machine learning algorithms
· Understanding the role of algorithms in data visualization
· Algorithms for streaming data
· Algorithms for practical use
· Demonstration and communication of algorithm use
· The presentation should include details that are pertinent to a nontechnical audience.
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