Objectives Data Acquaintance: Gain firsthand experience with the datasets, developing an understanding of their structure, significance, and potential applications in future assignments. Literary Foun

Objectives

Data Acquaintance: Gain firsthand experience with the datasets, developing an understanding of their structure, significance, and potential applications in future assignments.

Literary Foundations: Cultivate a solid theoretical base by delving into select academic papers, encapsulating their core messages and relevance to the broader machine-learning context.

Directions

Data Exploration: Access the provided datasets and review them meticulously. As these datasets will recur in subsequent assignments, grasp their format, variables, and overall nature.

Paper Review: Dive deep into the specified academic papers. Read analytically, discerning each paper's key points, methodologies, and conclusions.

Summary Creation: Post your readings and craft concise summaries encapsulating the essence of each paper, highlighting its significance, findings, and implications for the world of machine learning.

What to Include

Dataset Insights: Document any preliminary observations, questions, or insights you derive from the dataset review. This will be instrumental in your future assignments.

Paper Summaries: Include a well-articulated summary demonstrating your understanding and analytical reflections for each assigned paper (500 words for each paper).

Resources - everything has been attached in this post

Dataset Repository: Access the primary datasets you'll work with; 

https://www.mediafire.com/file/4t0mxewczhvfwsp/mof_casestudy.xlsx/file

https://www.mediafire.com/file/4c918wam1c7xi6y/thbmd_casestudy.xlsx/file

Reading List: Find the selected papers for review;

https://www.mediafire.com/file/66j6ec2bmri9k1r/s00223-020-00734-y_%25281%2529.pdf/file

https://www.mediafire.com/file/rza04w1kqjpmzb8/s41598-021-83828-3_%25282%2529.pdf/file

Embarking on this module is your first step into the multifaceted world of Machine Learning. This blend of hands-on dataset exploration and academic readings is crafted to build a robust foundation, setting the stage for the deeper dives to come. Enjoy the learning!