intuitions behind Data Science

The intuition behind loss function

A quick introduction to central limit theorem and why it helps data analysis

Thoughts on causality and the need for a control sample

Can we think of neural networks as layers of decisions with regression and classification at each layer?

What are the different types of data attributes?

Independence of the dependent variable

Generalizing the estimations of population parameters

Guessing the recipe of data!

How are decision trees trained and what is entropy?

What is the intuition behind cross-validation for estimating population parameters?

What is a population and what is a sample? What exactly do we want to do with them?

What is Machine Learning? What are supervised and unsupervised machine learning methods?

What is cosine similarity in multidimensional data?

What is PCA and what does it do?

Intuition behind latent features in singular value decomposition

Building recommendation systems using content - features of users and items

Building recommendation systems using observed interaction data

Why are recommendation systems important and how they are built?