Time complexity is an important concept to know for data science interviews because it can be used to evaluate the efficiency of an algorithm and the scalability of a solution. You never know when you might need this bit of knowledge, either on the job or at an interview!

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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In today's episode, we'll be discussing regularization, an important technique used to prevent overfitting in machine learning models. If you find these episodes helpful, please consider supporting us on Patreon at patreon.com/user?u=84843123.

Your support will help us continue to produce these episodes and improve the show. Thanks for listening!

In this episode, we will provide a high-level overview of cross-validation in machine learning. This concept is fundamental to understanding how to build effective machine learning models, and it is often covered in data science interviews.

If you find these episodes helpful, please consider supporting us on Patreon at patreon.com/user?u=84843123.

Your support will help us continue to produce these episodes and improve the show. Thanks for listening!

-----------------------------------------------------------

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!