intuitions behind Data Science

Loss Function

The intuition behind loss function

Central Limit Theorem

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

Causality and Control

Thoughts on causality and the need for a control sample

Neural Networks

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

Types of Data Attributes

What are the different types of data attributes?

Intercept

Independence of the dependent variable

Bias and Variance

Generalizing the estimations of population parameters

Linear Regression

Guessing the recipe of data!

Decision Trees and Entropy

How are decision trees trained and what is entropy?

Validation

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

Ground Truths in Data Science

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

Thoughts on Machine Learning

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

Cosine Similarity

What is cosine similarity in multidimensional data?

Principal Component Analysis

What is PCA and what does it do?

Latent Features

Intuition behind latent features in singular value decomposition

Recommendation Systems Using Content

Building recommendation systems using content - features of users and items

Recommendation Systems Using Observed Data

Building recommendation systems using observed interaction data

Recommendation Systems

Why are recommendation systems important and how they are built?