Practice for data scientist and ML engineering interviews by drilling coding ML algos and functions from scratch using Numpy, PyTorch, pure python.

ML Ninja

FAANG SQL 50

Data Engineering

DSA 150

MLE Coding 75

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Meta SQL

Stats Superhero

Pandas Pro

Mean Squared Error

Bias variance decomposition

Moving average forecast

Linear Regression

Activation function relu

Distance metrics L2 cosine

Batch normalization forward pass

Recall At K

Vector addition

Difference transformation

Cross validation

Precision Score

Vision model evaluation metrics

Image tensor representation

Feature clipping

Neural Network Regressor

Negative sampling

Outlier detection IQR

L1 and L2 norms

Truncated BPTT

Mean pooling of embeddings

Logistic Regression

Early stopping retrieval

TF IDF

Momentum update step

Neural Network Classifier

Support Vector Regression

Bootstrap confidence intervals

Batched query retrieval

Vector multiplication

Image normalization

Outlier detection Z score

Change of basis

Lasso Regression

Vector normalization

Linear SVM Classifier

CNN convolution operation

Sigmoid function

Top k embeddings retrieval

Dropout forward pass

Stable softmax

Confusion matrix for vision

Cost sensitive evaluation

Calibration error

Mean Absolute Error

Mean Absolute Percentage Error

Convolution Layer

Sliding window detection

Binning continuous variables

Min max normalization

Lag features

Masked attention scores

LSTM gates forward

Early stopping criterion

Matrix transpose

Ensemble averaging

Removing constant features

Perplexity

Low rank matrix approximation

Edge detection filters

Detecting duplicate features

Gradient Boosting Classifier

Backpropagation through linear layer

Similarity ranking

Dot product

Softmax

Root Mean Squared Error

Multi layer perceptron inference

K Nearest Neighbors Classifier

Autocorrelation Function

Mean aggregation

Moving Average

Feature standardization

Chunk-level retrieval

Elastic Net Classifier

Lasso Classifier

False Positive Rate

Gradient Boosting Regressor

Mean Reciprocal Rank

Attention score

Embedding collapse detection

Multi-head concat

Ridge Regression

Elastic Net Regression

F1 Score

Activation function leaky relu

Linear Discriminant Analysis Classifier

Decision Tree Classifier

High cardinality categoricals

Cumulative sum features

Perceptron forward pass

Image augmentation transforms

Attention-weighted value sum

RNN forward pass

Multi-head split

Minimize 1D quadratic Closed Form

Threshold optimization

Mean and median imputation

SGD update step

K Means Clustering

ML Ninja

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