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Dimensionality reduction is a commonly asked about topic in Data Science Interviews. We'll go over the high-level reasons for using dimensionality reduction techniques as well as go into detail on PCA.
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Your donation goes directly to supporting this channel and the human labor that goes into each of these episodes. For each episode, we do research and fact-check our content to make sure that you get the best information possible, even on cutting edge topics. Become a Paid Subscriber: https://podcasters.spotify.com/pod/show/data-science-interview/subscribe
Becoming a premium member also gives you access to our locked episodes, which include helpful content such as:
- NLP
- Deep Learning
- Recurrent Neural Networks
- Imbalanced Data
- The Bias-Variance Tradeoff
- Transformers in NLP
- Self-Attention in NLP
- Distributions
- Statistics
- A/B Testing
- ROC-AUC
.... and so much more!
By Data Science Interview Prep Podcast5
1616 ratings
Dimensionality reduction is a commonly asked about topic in Data Science Interviews. We'll go over the high-level reasons for using dimensionality reduction techniques as well as go into detail on PCA.
-----------------------------------------------------------
If you enjoy our podcast, please consider becoming a premium member on either Patreon ($5 donation) or Spotify ($2.99 donation).
Your donation goes directly to supporting this channel and the human labor that goes into each of these episodes. For each episode, we do research and fact-check our content to make sure that you get the best information possible, even on cutting edge topics. Become a Paid Subscriber: https://podcasters.spotify.com/pod/show/data-science-interview/subscribe
Becoming a premium member also gives you access to our locked episodes, which include helpful content such as:
- NLP
- Deep Learning
- Recurrent Neural Networks
- Imbalanced Data
- The Bias-Variance Tradeoff
- Transformers in NLP
- Self-Attention in NLP
- Distributions
- Statistics
- A/B Testing
- ROC-AUC
.... and so much more!

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