intuitions behind Data Science
Ashay Javadekar
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No math, no equations, just intuitions behind Data Science.
Available episodes
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Oldest first
Dec 13, 2021
Loss Function
The intuition behind loss function
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Dec 3, 2021
Central Limit Theorem
A quick introduction to central limit theorem and why it helps data analysis
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Dec 2, 2021
Causality and Control
Thoughts on causality and the need for a control sample
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Dec 1, 2021
Neural Networks
Can we think of neural networks as layers of decisions with regression and classification at each layer?
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Nov 29, 2021
Types of Data Attributes
What are the different types of data attributes?
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Nov 23, 2021
Intercept
Independence of the dependent variable
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Nov 23, 2021
Bias and Variance
Generalizing the estimations of population parameters
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Nov 19, 2021
Linear Regression
Guessing the recipe of data!
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Nov 19, 2021
Decision Trees and Entropy
How are decision trees trained and what is entropy?
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Nov 17, 2021
Validation
What is the intuition behind cross-validation for estimating population parameters?
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Nov 16, 2021
Ground Truths in Data Science
What is a population and what is a sample? What exactly do we want to do with them?
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Nov 15, 2021
Thoughts on Machine Learning
What is Machine Learning? What are supervised and unsupervised machine learning methods?
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Nov 12, 2021
Cosine Similarity
What is cosine similarity in multidimensional data?
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Nov 10, 2021
Principal Component Analysis
What is PCA and what does it do?
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Nov 9, 2021
Latent Features
Intuition behind latent features in singular value decomposition
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Nov 8, 2021
Recommendation Systems Using Content
Building recommendation systems using content - features of users and items
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Nov 4, 2021
Recommendation Systems Using Observed Data
Building recommendation systems using observed interaction data
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Nov 4, 2021
Recommendation Systems
Why are recommendation systems important and how they are built?
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