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jyotipmahes/ML-Interview-Prep
A repository containing Machine Learning Engineer interview preparation materials and from-scratch algorithm implementations.
The repository serves as a collection of interview preparation materials and from-scratch algorithm implementations for Machine Learning Engineers. The repository provides from-scratch algorithm implementations with mathematical formulations and verification tests, using pure Python or NumPy/PyTorch depending on the notebook.
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Claims below include README evidence. A matching quote or worker agreement does not establish that a claim is correct. Category majority among 2 workers. Version 1.
A repository containing Machine Learning Engineer interview preparation materials and from-scratch algorithm implementations.
Repository for Machine Learning Engineer (MLE) interview preparation materials and from-scratch algorithm implementations
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
The repository serves as a collection of interview preparation materials and from-scratch algorithm implementations for Machine Learning Engineers.
Repository for Machine Learning Engineer (MLE) interview preparation materials and from-scratch algorithm implementations
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
The repository provides from-scratch algorithm implementations with mathematical formulations and verification tests, using pure Python or NumPy/PyTorch depending on the notebook.
From-scratch implementations of core MLE coding interview algorithms with mathematical formulations, interview tips, and verification tests
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Includes from-scratch implementations of Linear Regression using Vectorized Gradient Descent and the Normal Equation.
Linear Regression (Vectorized Gradient Descent & Normal Equation)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Provides Logistic Regression implementations with Vectorized Gradient Descent and a Numerically Stable Sigmoid function.
Logistic Regression (Vectorized Gradient Descent & Numerically Stable Sigmoid)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Features K-Means Clustering with Random Initialization, Vectorized Broadcasting, and List Comprehension.
K-Means Clustering (Random Initialization, Vectorized Broadcasting & List Comprehension)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Implements K-Nearest Neighbors using a Unified Pairwise Distance Matrix and O(N) Top-K selection.
K-Nearest Neighbors (KNN) (Unified Pairwise Distance Matrix & $\mathcal{O}(N)$ Top-K)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Contains a 2-Layer MLP with Backpropagation using Manual Multivariate Chain Rule in pure NumPy.
2-Layer MLP with Backprop (Manual Multivariate Chain Rule in pure NumPy)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Includes Multi-Head Self-Attention with Scaled Dot-Product, Causal Masking, and Projections in PyTorch.
Multi-Head Self-Attention (Scaled Dot-Product, Causal Masking & Projections in PyTorch)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Provides Low-Rank Adaptation (LoRA) as a PEFT module with initialization trap handling and weight merging.
Low-Rank Adaptation (LoRA) (PEFT module with initialization trap handling & weight merging)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Implements Byte-Pair Encoding (BPE) as a Subword tokenizer with pair-frequency statistics and greedy merging.
Byte-Pair Encoding (BPE) (Subword tokenizer with pair-frequency statistics and greedy merging)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Offers LeetCode-style algorithmic questions intersecting with Machine Learning, Probability, Retrieval, and Computer Vision in pure Python.
LeetCode-style algorithmic questions intersecting with Machine Learning, Probability, Retrieval, and Computer Vision in pure Python
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Includes practical implementation and AI-assisted coding patterns asked in companies like Shopify, LinkedIn, and Meta.
Practical implementation and AI-assisted coding patterns (asked in companies like Shopify, LinkedIn, Meta, and similar tech organizations
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Features stateful simulation, robot navigation, game state machines, and progressive class design in pure Python.
stateful simulation, robot navigation, game state machines, and progressive class design in pure Python
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Includes vectorized gradient descent, normal equations, and numerically stable sigmoid implementations for linear and logistic regression.
From-scratch implementations of core MLE coding interview algorithms with mathematical formulations, interview tips, and verification tests
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Provides both NumPy and PyTorch implementations of layer normalization for neural network training.
Layer Normalization (NumPy & PyTorch implementations)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Implements multi-head self-attention mechanisms with causal masking and projection layers using PyTorch.
Multi-Head Self-Attention (Scaled Dot-Product, Causal Masking & Projections in PyTorch)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Features a PEFT module for LoRA with specific handling for initialization traps and weight merging techniques.
Low-Rank Adaptation (LoRA) (PEFT module with initialization trap handling & weight merging)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Implements a subword tokenizer using byte-pair encoding with pair-frequency statistics and greedy merging strategies.
Byte-Pair Encoding (BPE) (Subword tokenizer with pair-frequency statistics and greedy merging)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Solves algorithmic problems related to ML, probability, and computer vision using only standard Python libraries without NumPy or PyTorch.
LeetCode-style algorithmic questions intersecting with Machine Learning, Probability, Retrieval, and Computer Vision in pure Python
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Implements weighted random sampling using prefix sums and binary search techniques for efficient data selection.
Weighted Random Sampling (Prefix Sums + Binary Search / LC 528)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Calculates exact ROC-AUC scores with proper handling of ties using the Wilcoxon Rank-Sum statistic.
Exact ROC-AUC with Tie Handling (Wilcoxon Rank-Sum Statistic & Fractional Ties)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
Builds a task execution engine for directed acyclic graphs using Kahn's algorithm for topological sorting and cycle detection.
DAG Task Execution Engine with Dependency Resolution (Directed acyclic graph, Kahn's topological sort, circular cycle detection, and task output piping)
Quote matched the README or GitHub description at extraction. Same claim and quote from 1 worker(s). Interpretation not reviewed by a maintainer.
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