Machine Learning and Data Science progression: what I did yesterday and today.

What I did yesterday:
• DS101X Statistical Thinking for Data Science and Analytics
// completion of week 2 “Probability and Statistics I”
# introduction to statistics
# normal (gaussian) distribution
# variance/standard deviation
# statistical inference
# what is ‘statistical significance’ -> how to distinguish between a meaningful association between factors and outcomes from noise or random chance
# correlation vs causation
# what do we mean by ‘confidence interval’
# dangers and pitfalls of p-score reliance
# conditional probability and Bayes’ Theorem
# chi-square test for measuring independence between common attributes

What I did today:
• Stanford Machine Learning Course on Coursera
// completion of week 1
# Introduction
# What is Machine Learning?
# Model
-> We start with a basic univariate model (order=1)
-> Learning algorithms: Supervised vs Unsupervised
-> Regression vs Classification problems
# Cost function (error function)
-> When defining our model, we seek to minimize the sum of squared-errors.
# Gradient Descent for Linear Regression

This is a useful reference and companion learning resource:
http://www.holehouse.org/mlclass/01_02_Introduction_regression_analysis_and_gr.html