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Published by Ashay Javadekar
No math, no equations, just intuitions behind Data Science.
On the charts
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From the feed
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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?
Ranking source
Apple Podcasts rankings via the Mato Topic Intelligence Platform.
Observed September 20, 2026.
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