MLND

3e Project: Model evaluation & validation

Project details

DESCRIPTION:

use Boston house price dataset to PREDICT selling price of a new/unseen home

PROCESS:

EXPLORE data > obtain important FEATURES & DESCRIPTIVE statistics abt data
Properly SPLIT dataset into TRAINING & TEST datasets
DETERMINE suitable PERFORMANCE METRIC for evaluating the problem
ANALYSE performance graphs for learning algorithm over varying TRAINING SET SIZES & with varying number of PARAMETERS
CHOOSE OPTIMAL MODEL that best generalises unseen data
TEST chosen optimal model on a NEW SAMPLE & COMPARE PREDICTED selling price to ACTUAL statistics

Dipping a toe in the Udacity pool:
I received an email from the UK/Europe Business Growth Manager of Udacity, looking for people with knowledge of/experiencein Machine Learning to lead and/or support face-to-face training sessions in Machine Learning in London. The email included the link to Udacity’s Nano Degree in Machine Learning.

While I’m not yet skilled or experienced enough at this time to lead a session, I felt really excited that I might have an opportunity to be a session supporter and assistant. That would be cool as I’d be exploring and networking in such an important growth area in tech skills, as well as supporting the growth of MOOCs here in London, which is something I feel really passionately about. [Read more…] about Machine Learning Engineering