Discoverintuitions behind Data Science
intuitions behind Data Science
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intuitions behind Data Science

Author: Ashay Javadekar

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No math, no equations, just intuitions behind Data Science.

18 Episodes
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Loss Function

Loss Function

2021-12-1306:13

The intuition behind loss function
Central Limit Theorem

Central Limit Theorem

2021-12-0405:21

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

Causality and Control

2021-12-0307:02

Thoughts on causality and the need for a control sample
Neural Networks

Neural Networks

2021-12-0108:34

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?
Intercept

Intercept

2021-11-2307:29

Independence of the dependent variable
Bias and Variance

Bias and Variance

2021-11-2306:20

Generalizing the estimations of population parameters
Linear Regression

Linear Regression

2021-11-1907:02

Guessing the recipe of data!
How are decision trees trained and what is entropy?
Validation

Validation

2021-11-1709:04

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?
Cosine Similarity

Cosine Similarity

2021-11-1208:46

What is cosine similarity in multidimensional data?
What is PCA and what does it do?
Latent Features

Latent Features

2021-11-0910:29

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
Recommendation Systems

Recommendation Systems

2021-11-0406:53

Why are recommendation systems important and how they are built?
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