7 Super Cheat Sheets You Need To Ace Machine Learning Interview

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7 Super Cheat Sheets You Need To Ace Machine Learning Interview
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In this post, you will learn about machine learning and deep learning algorithms and frameworks. Furthermore, you will learn tips and tricks on how to handle the data, select metrics, and improve the model performance. 

The last and most essential cheat sheet is about machine learning interview questions and answers with visual examples. 

The Machine Learning Algorithms cheat sheet is all about algorithm’s description, applications, advantages, and disadvantages. It is your gateway into the world of supervisor and unsupervised machine learning models, where you will learn about linear and tree-based models, clustering, and association. 

 

7 Super Cheat Sheets You Need To Ace Machine Learning Interview
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The cheat sheet consists of:

  1. Linear Regression
  2. Logistic Regression
  3. Ridge Regression
  4. Lasso Regression
  5. Decision Tree
  6. Random Forests
  7. Gradient Boosting Regression
  8. XGBoost
  9. LightGBM Regressor
  10. K-Means
  11. Hierarchical Clustering
  12. Gaussian Mixture Models
  13. Apriori Algorithm

The Scikit-learn Cheat Sheet For Machine Learning consists of Scikit-learn’s API for loading the data, splitting the data, supervised and unsupervised models, prediction, model evaluation, and model tuning. 

You will learn about processing the data, feature engineering, applying various models, and improving the model performance using Grid search. 

 

7 Super Cheat Sheets You Need To Ace Machine Learning Interview
Image from Cheat Sheet
 

The cheat sheet consists of:

  1. Loading the data
  2. Training and test data 
  3. Preprocessing the data 
  4. Supervised learning model 
  5. Unsupervised learning model 
  6. Model fitting 
  7. Prediction 
  8. Evaluation
  9. Cross-validation
  10. Model tuning 

The Machine Learning Tips and Tricks cheat sheet is all about model metrics, model selection, and evaluation. It is a web-based cheat sheet by Stanford University where you can learn about classification and regression, cross-validation and regularization, and basis and variance tradeoff. 

 

7 Super Cheat Sheets You Need To Ace Machine Learning Interview
Image from Cheat Sheet
 

The cheat sheet consists of:

  1. Classification metrics
  2. Regression metrics
  3. Model selection
  4. Diagnostics 

The Deep Learning Super VIP cheat sheet is explaining various components of deep learning using diagrams and mathematics. You will learn about convolutional neural networks, recurrent neural networks, deep learning tips, and tricks, and go deep into computer vision and NLP models.  

 

7 Super Cheat Sheets You Need To Ace Machine Learning Interview
Image from Cheat Sheet
 

The cheat sheet consists of:

  1. Types of neural networks layer
  2. Filter hyperparameters
  3. Tuning hyperparameters
  4. Commonly used activation functions
  5. Object detection
  6. Handling long term dependencies
  7. Learning word representation
  8. Comparing words
  9. Language model
  10.  Machine translation
  11.  Attention
  12.  Data processing
  13.  Training a neural network
  14.  Parameter tuning
  15.  Regularization

In the Keras: Neural Networks in Python cheat sheet, you will learn to process and prepare data for neural network models. Moreover, you will learn to build model architecture, compile it, train it, tune it, and perform the model evaluation. 

The cheat sheet is a quick way to revise Keras’s commands and learn new things. 

 

7 Super Cheat Sheets You Need To Ace Machine Learning Interview
Image from Cheat Sheet
 

The cheat sheet consists of:

  1. Loading the data
  2. Preprocessing
  3. Model architecture
  4. Prediction
  5. Inspect model
  6. Compile model
  7. Model training
  8. Model evaluation
  9. Saving and reloading models
  10. Model finetuning

The PyTorch official cheat sheet consists of commands and API for handling the data and building deep learning models. It is a straightforward API for experienced Pytroch users.  

 

7 Super Cheat Sheets You Need To Ace Machine Learning Interview
Image from Cheat Sheet
 

The cheat sheet consists of:

  1. Imports: Neural Network API, Torchscript and JIT, ONNX, Vision, and Distributed Training.
  2. Tensors: Creation, Dimensionality, Algebra, and GPU Usage.
  3. Deep Learning: Loss Functions, Activation Functions, Optimizers, and Learning rate scheduling.
  4. Data Utilities: Datasets, Dataloaders, and DataSamplers. 

In the Cheat Sheets for Machine Learning Interview, the author has explained the most asked questions during machine learning interviews with the help of graphical representation. The cheat sheet will help you ace your machine learning interview by preparing you about various machine learning algorithms, issues, trade-offs, data processing, and model tuning. 

 

7 Super Cheat Sheets You Need To Ace Machine Learning Interview
Image from Cheat Sheets
 

The interview cheat sheet consists of:

  1. Bias and Variance
  2. Imbalanced Data
  3. Bayes’ Theorem
  4. Dimensionality Reduction
  5. Regression
  6. Regularization
  7. Convolutional Neural Network
  8. Famous DNNs
  9. Ensemble Learning
  10. Autoencoder and Variational Autoencoder

Besides cheat sheets, you can read books, take coding assessment tests, and even take a mock interview session with your colleague to increase your odds of getting past the interview stage. 

I will highly recommend you to read Ace the Data Science Interview book and take the LeetCode 75 study plan for data science and machine learning interviews. 

If you like my work, do share it on social media, or if you have questions regarding the career, you can reach out to me on LinkedIn

 
 
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a bachelor’s degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
 

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