# Cheat Sheets for Machine Learning Interview Topics

Updates:Dec 25, 2021: Added Auto Encoder and variational Encoder

Dec 25, 2020: Added Ensemble Methods

Download the updated version of the cheat sheets from http://cheatsheets.aqeel-anwar.com/

A couple of years ago I started applying for internships in the area of Machine Learning and ML system design. I had been studying and actively researching in the area of ML for a few years then. I was familiar with most of the basic topics. But when I started interviewing, I realized that though I had a general understanding of the topics, I required a quick go-through before I can answer it perfectly.

So I decided to refresh my concepts. I realized that before every interview, I was required to go through the topics again. So, I created my handwritten notes. Skimming through them was much easier than going through slides and book chapters. It provided me with a quick boost to my understanding in a short amount of time. I decided to convert my hand-written notes into compact cheat sheets that might come in handy for ML interviews and daily data-scientist life in general.

The rest of the article is based on those cheat sheets. For each topic, I provide

- An overview in form of a cheat sheet
- Example interview questions
- Suggested articles for a detailed understanding of the topic.

Note 1:These cheat sheets are aimed at refreshing the concepts and are not meant to provide in-depth understandings of the topics for beginners.

Note 2:The article is constantly updated for more cheat sheets.

Source:All of these cheat sheets (and more) can be downloaded in pdf format fromwww.cheatsheets.aqeel-anwar.com.

# Bias and Variance in Machine Learning Models

## a) Overview:

## b) Example Questions:

- What is Bias in ML models?
- What is Variance in ML models?
- What is the trade-off between bias and variance?
- What are the demerits of a high bias / high variance ML model?
- How do you select the model (high bias or high variance) based on the training data size?

**c) Detailed Article:**

# Imbalanced data in Machine Learning

## a) Overview:

## b) Example Questions:

- What is imbalanced data in classification?
- Is accuracy a good performance metric? When does it fail to capture the performance of an ML system?
- What are Precision and Recall? Give an example
- How to address the issue of imbalanced data?

## c) Detailed Articles:

# Bayes’ Theorem

## a) Overview:

## b) Example Questions:

- What is Bayes’ theorem?
- Toy example to implement Bayes’ theorem
- What is the difference between MLE and MAP?
- When are MAP and MLE equal?

## c) Detailed Articles:

# Principal Component Analysis and Dimensionality Reduction

## a) Overview:

## b) Example Questions:

- What is Principal Component Analysis?
- How can we use PCA to reduce dimensions?
- What do the eigenvalues signify in the context of PCA?
*(Greater the magnitude of eigenvalue, the more information is preserved if we keep that corresponding eigenvector as a feature vector for our data)*

## c) Detailed Articles:

# Regression in Machine Learning

## a) Overview:

## b) Example Questions:

- What is Regression in ML?
- How can we introduce regularization in regression?
*(LASSO and Ridge)* - What impact does LASSO and Ridge regression has on the weights of the model?
*(Ridge tries to reduce the size of the weights learned, whereas LASSO tries to force them to zero creating a more sparse set of weights)* - When does the prediction by Bayesian linear regression approach the prediction of linear regression?
*(When the number of data points is large enough)* - Is logistic regression a misnomer?
*(Yes, because it is not regression, but classification based on regression)*

## c) Detailed Articles:

# Regularization in Machine Learning

## a) Overview:

## b) Example Questions:

- What is regularization in ML?
- How can we address over-fitting?
- What is K-fold cross-validation?
- What is the difference between L1 and L2 regularization?
- Why do we use dropout?

## c) Detailed Articles:

# Basics of Convolutional Neural Network

## a) Overview:

## b) Example Questions:

- What is CNN?
- Explain the difference between the convolutional layer and transposed convolutional layer.
- What are some of the loss functions used for classification?

## c) Detailed Article:

# Famous DNNs in Machine Learning

## a) Overview:

## b) Example Questions:

- How does the ResNet network address the problem of vanishing gradient?
- What is one of the main key features of the Inception Network?
- What are shortcut connections in the ResNet network?

## c) Detailed Articles:

# Ensemble Methods in Machine Learning

## a) Overview:

## b) Example Questions:

- What is Ensemble learning?
- What is bagging, boosting, and stacking in ML?
- What is the difference between bagging and boosting?
- Name a few boosting methods

## c) Detailed Articles:

# Autoencoder and Variational Autoencoder

## a) Overview:

## b) Example Questions:

- What is an Autoencoder?
- Is the latent space of Autoencoder regularised?
- What is the loss function for a variational autoencoder?
- Whats the difference between an Autoencoder and Variational Autoencoder?

## c) Detailed Articles:

# Summary

This article provides a list of cheat sheets covering important topics for a Machine learning interview followed by some example questions. The list of topics and the number of cheat sheets are constantly being added to the article.

**If this article was helpful to you, feel free to clap, share and respond to it. If you want to learn more about Machine Learning and Data Science, follow me @****Aqeel Anwar**** or connect with me on ***LinkedIn**.*