The Neherlab COVID-19 forecast model Basic assumptions Overview Age cohorts Severity Seasonality Transmission reduction Details of the model Population compartments Model parameters Infection After infection Load data Initialise parameters Fixed constants Infrastructure Parameter vector Population Parameters vector Differential equation solver Bilibliography The Neherlab COVID-19 forecast model using CSV, Dates; using DataFrames, DataFramesMeta; using Plots, PyPlot; using DifferentialEquations; This is more a data science post than machine learning.
Both capstones for the HarvardX certificates are now available. Just click on the Projects link!
If Gitbooks are not your thing, at the top of their main page, there is a download link to a pdf version.
After 3 months of work, the final report for the HarvardX Data Science course was submitted.
It is based on the LendingClub dataset. LendingClub is a peer-2-peer lender. This is a matching of private borrowers and investors.
I recently finished to penultimate final assignment for the HarvardX Data Science course. The Stanford course was clearly machine learning. This one is definitely lighter on the machine learning and much heavier on the data science: how to source, clean and visualise data are key skills.