intuitions behind Data Science

<p>No math, no equations, just intuitions behind Data Science.</p>

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Loss Function

The intuition behind loss function

12-13

06:13

Central Limit Theorem

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

12-04

05:21

Causality and Control

Thoughts on causality and the need for a control sample

12-03

07:02

Neural Networks

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

12-01

08:34

Types of Data Attributes

What are the different types of data attributes?

11-29

13:00

Intercept

Independence of the dependent variable

11-23

07:29

Bias and Variance

Generalizing the estimations of population parameters

11-23

06:20

Linear Regression

Guessing the recipe of data!

11-19

07:02

Decision Trees and Entropy

How are decision trees trained and what is entropy?

11-19

06:29

Validation

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

11-17

09:04

Ground Truths in Data Science

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

11-16

08:19

Thoughts on Machine Learning

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

11-16

06:23

Cosine Similarity

What is cosine similarity in multidimensional data?

11-12

08:46

Principal Component Analysis

What is PCA and what does it do?

11-11

10:46

Latent Features

Intuition behind latent features in singular value decomposition

11-09

10:29

Recommendation Systems Using Content

Building recommendation systems using content - features of users and items

11-08

08:15

Recommendation Systems Using Observed Data

Building recommendation systems using observed interaction data

11-04

09:49

Recommendation Systems

Why are recommendation systems important and how they are built?