intuitions behind Data Science

About intuitions behind Data Science

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

Publisher

Ashay Javadekar

Category

science

Language

en

Explicit

No

First episode

4 Nov 2021

Latest episode

13 Dec 2021

Latest episodes

18 episodes in the feed.

1. 

   13 Dec 2021

   Loss Function

   The intuition behind loss function

2. 

   4 Dec 2021

   Central Limit Theorem

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

3. 

   3 Dec 2021

   Causality and Control

   Thoughts on causality and the need for a control sample

4. 

   1 Dec 2021

   Neural Networks

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

5. 

   29 Nov 2021

   Types of Data Attributes

   What are the different types of data attributes?

6. 

   23 Nov 2021

   Intercept

   Independence of the dependent variable

7. 

   23 Nov 2021

   Bias and Variance

   Generalizing the estimations of population parameters

8. 

   19 Nov 2021

   Linear Regression

   Guessing the recipe of data!

9. 

   19 Nov 2021

   Decision Trees and Entropy

   How are decision trees trained and what is entropy?

10. 

    17 Nov 2021

    Validation

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

11. 

    16 Nov 2021

    Ground Truths in Data Science

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

12. 

    16 Nov 2021

    Thoughts on Machine Learning

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

13. 

    12 Nov 2021

    Cosine Similarity

    What is cosine similarity in multidimensional data?

14. 

    11 Nov 2021

    Principal Component Analysis

    What is PCA and what does it do?

15. 

    9 Nov 2021

    Latent Features

    Intuition behind latent features in singular value decomposition

16. 

    8 Nov 2021

    Recommendation Systems Using Content

    Building recommendation systems using content - features of users and items

17. 

    4 Nov 2021

    Recommendation Systems Using Observed Data

    Building recommendation systems using observed interaction data

18. 

    4 Nov 2021

    Recommendation Systems

    Why are recommendation systems important and how they are built?

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

Apple Podcasts (US)

4.0 / 5

8 ratings

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