I have a presentation coming up, and I need someone to help explain certain machine learning and SNA techniques and theories.
Here are some examples of things I don't quite understand:
- Why use log-log scale when presenting data sometimes?
- What is the power law?
- cumulative advantage process?
- preferential attachments by barabasi?
In Machine Learning:
- When building a unified framework of data, how can we organize it using Martix Factorization?
- What other machine learning techniques can be used in a very large and unorganized, unstructured dataset?
- When building a recommender system, they are usually built using: content-based recommendations, collaborative recommendations, and a hybrid approach. I need to understand what is meant here.
I just need to understand enough to be able to answer questions during the presentation of the attached proposal document.
The above are the main questions, there might be a couple more along the way that I might have.
I'm looking for someone who is willing to arrange for a skype chat with me sometime tomorrow or Sat or Sunday morning to sit and discuss any questions I might have. I will pay be the hour, Depending on how it goes, it might be one session or more.
If interested kindly let me know and I will email you the document.