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intuitions behind Data Science
Published by Ashay Javadekar
- Science
- Mathematics
No math, no equations, just intuitions behind Data Science.
Listen on Apple PodcastsPublished by Ashay Javadekar
No math, no equations, just intuitions behind Data Science.
Listen on Apple Podcasts6 min
The intuition behind loss function
5 min
A quick introduction to central limit theorem and why it helps data analysis
7 min
Thoughts on causality and the need for a control sample
8 min
Can we think of neural networks as layers of decisions with regression and classification at each layer?
13 min
What are the different types of data attributes?
7 min
Independence of the dependent variable
6 min
Generalizing the estimations of population parameters
7 min
Guessing the recipe of data!
6 min
How are decision trees trained and what is entropy?
9 min
What is the intuition behind cross-validation for estimating population parameters?
8 min
What is a population and what is a sample? What exactly do we want to do with them?
6 min
What is Machine Learning? What are supervised and unsupervised machine learning methods?
8 min
What is cosine similarity in multidimensional data?
10 min
What is PCA and what does it do?
10 min
Intuition behind latent features in singular value decomposition
8 min
Building recommendation systems using content - features of users and items
9 min
Building recommendation systems using observed interaction data
6 min
Why are recommendation systems important and how they are built?