Description
**Task 2: Set up your blog space for the final project. This is where you will be writing about your final projects. Go to the following spreadsheet and report your group members and blog address. Your blog should have the contents of Task 1 and 3 for now.
https://docs.google.com/spreadsheets/d/13mAXLFv7qqzLF5ntvH0MzThc6Kh0T8yt3Xk9bXhVtac/edit?usp=sharing
Blog Sites:
https://www.github.com
https://www.medium.com or any other tech blog/blogging sites.
**Task 3: Write your research proposal (< 200 words) including the title. Title:
Proposal:
Submission: Update the google doc, convert your blog to PDF, and submit to LMS (one submission per group).
*** Possible Topic Proposal? (Option A or B, Combining AI speaker + LG appliances):
When you choose your topic to work on, you can choose one of the following options:
Option A: Take a very similar step like Kaggle’s Titanic example, you will show us how to investigate the dataset of your choice. You can choose one of the methods from the AI/Machine learning/DL libraries (e.g., https://scikit-learn.org/stable/, R – https://cran.r- project.org/web/views/MachineLearning.html, Weka – https://www.cs.waikato.ac.nz/ml/weka/, Neural Network packages, AWS Sage maker https://aws.amazon.com/sagemaker/, and many others.). You will show us a step-by-step procedure on how to do learning. Accuracy of your method does not matter in this project.
Option B: Using NUGU AI Speaker (perhaps) using some of techniques from Option A. The idea is to present the analysis method nicely to the public. AI + LG is also recommended. We will discuss more later!
Tech Blog Samples (more available on our spreadsheet):
https://blog.google/technology/ai/
https://www.kaggle.com/mrisdal/exploring-survival-on-the-titanic/report https://medium.com/about-developer-blog/xgboost-gone-wild-predicting-returns-with-extreme-gradient-boosting-3e2c16c5bc01
https://blog.cloudflare.com/how-to-receive-a-million-packets/ https://towardsdatascience.com/machine-learning/home
