# alirezadir/machine-learning-interviews

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7,807 stars · 1,403 forks · Jupyter Notebook · mit

## Links

- GitHub: https://github.com/alirezadir/Machine-Learning-Interviews
- awesome-repositories: https://awesome-repositories.com/repository/alirezadir-machine-learning-interviews.md

## Topics

`agentic` `ai` `ai-agents` `ai-engineering` `deep-learning` `interview` `interview-practice` `interview-preparation` `interviews` `machine-learning` `machine-learning-algorithms` `scalable-applications` `system-design`

## Description

This project is a comprehensive machine learning interview guide and technical study resource designed for individuals preparing for machine learning and AI engineering roles. It provides a collection of materials and practice problems covering core algorithms, theoretical fundamentals, and the implementation of neural network architectures.

The resource serves as a technical reference for generative AI development, focusing on the design and optimization of large language models and diffusion systems. It includes frameworks for system design, covering the architecture of production machine learning pipelines, retrieval pipelines, agentic workflows, and the reduction of latency and memory footprints through inference optimization.

Beyond model architecture, the project covers MLOps deployment workflows, including A/B testing and canary releases, as well as model evaluation and validation strategies. It also provides coaching for behavioral interviews, utilizing structured communication frameworks to handle professional and situational questions.

The project is implemented as a collection of Jupyter Notebooks.

## Tags

### Education & Learning Resources

- [System Design Interview Preparation](https://awesome-repositories.com/f/education-learning-resources/system-design-interview-preparation.md) — Serves as a comprehensive guide for preparing for machine learning system design interviews. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))
- [ML Interview Preparation](https://awesome-repositories.com/f/education-learning-resources/technical-interview-preparation/ml-interview-preparation.md) — Serves as a comprehensive study resource for passing technical assessments in machine learning and AI engineering roles.
- [Algorithm Implementations](https://awesome-repositories.com/f/education-learning-resources/algorithm-implementations.md) — Provides pedagogical code implementations of core machine learning and neural network architectures for technical assessments. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLC/ml-coding.md))
- [Behavioral Interview Coaching](https://awesome-repositories.com/f/education-learning-resources/behavioral-interview-coaching.md) — Offers coaching on structured communication frameworks and strategies for navigating the behavioral aspects of technical hiring.
- [Coding Interview Preparation](https://awesome-repositories.com/f/education-learning-resources/coding-interview-preparation.md) — Provides targeted practice with algorithms and data structures specifically for technical machine learning interviews. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/README.md))
- [Generative AI Technical References](https://awesome-repositories.com/f/education-learning-resources/curricula-instructional-design/curricula-roadmaps/ai-machine-learning-roadmaps/generative-ai-curricula/model-fine-tuning-guides/generative-ai-technical-references.md) — Provides technical guidance on optimizing generative models using RLHF, DPO, and parameter-efficient tuning.
- [Algorithmic Patterns](https://awesome-repositories.com/f/education-learning-resources/educational-resources/algorithms-theory-academics/algorithm-data-structure-guides/algorithmic-patterns.md) — Teaches recurring algorithmic patterns and templates to solve complex technical interview problems. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/lc-coding.md))
- [Interview Preparation](https://awesome-repositories.com/f/education-learning-resources/interview-preparation.md) — Covers the internal mechanics and core concepts of foundation models for technical interview preparation. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/README.md))
- [Machine Learning Algorithm Study Guides](https://awesome-repositories.com/f/education-learning-resources/machine-learning-algorithm-study-guides.md) — Provides comprehensive study materials on supervised, unsupervised, and semi-supervised machine learning algorithms. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [Interview Study Guides](https://awesome-repositories.com/f/education-learning-resources/machine-learning-guides/interview-study-guides.md) — Provides a comprehensive collection of technical study materials and practice problems for AI engineering roles.
- [Generative AI System Design](https://awesome-repositories.com/f/education-learning-resources/system-design-interview-preparation/generative-ai-system-design.md) — Offers frameworks for architecting generative AI systems using retrieval pipelines and agentic workflows. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))
- [Behavioral Interview Preparation](https://awesome-repositories.com/f/education-learning-resources/interview-preparation-resources/behavioral-interview-preparation.md) — Offers coaching and frameworks for answering situational and behavioral questions during professional hiring processes. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/README.md))
- [Model Regularization Tutorials](https://awesome-repositories.com/f/education-learning-resources/model-regularization-tutorials.md) — Provides educational content on preventing overfitting through regularization methods like Lasso and Ridge. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [MLOps Guides](https://awesome-repositories.com/f/education-learning-resources/model-training-guides/mlops-guides.md) — Provides instructional material on MLOps strategies, including canary releases and performance monitoring.

### Artificial Intelligence & ML

- [Contrastive Learning Models](https://awesome-repositories.com/f/artificial-intelligence-ml/contrastive-learning-models.md) — Covers the implementation of contrastive learning for aligning multimodal data embeddings.
- [Deep Learning Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-architectures.md) — Includes practice problems for implementing various deep learning architectures including transformers and recurrent networks. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [Generative AI Development](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai-development.md) — Provides a technical reference for building and optimizing generative AI applications using RAG and RLHF.
- [LLM Application Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-application-frameworks.md) — Presents a structured approach to architecting LLM applications using retrieval pipelines and agentic workflows.
- [Preference-Based Model Alignments](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/fine-tuning-and-alignment/preference-based-model-alignments.md) — Explains post-training strategies like RLHF and DPO to align model outputs with human preferences. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [Multi-Head Attention Mechanisms](https://awesome-repositories.com/f/artificial-intelligence-ml/multi-head-attention-mechanisms.md) — Provides technical study materials on multi-head attention mechanisms used in transformer architectures.
- [Natural Language Processing](https://awesome-repositories.com/f/artificial-intelligence-ml/natural-language-processing.md) — Includes practical implementations of natural language processing techniques and algorithms to solve coding challenges. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLC/ml-coding.md))
- [Parameter Adaptation Techniques](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-adaptation-techniques.md) — Explains low-rank adaptation (LoRA) as a parameter-efficient fine-tuning technique.
- [Parameter Efficient Fine-Tuning](https://awesome-repositories.com/f/artificial-intelligence-ml/parameter-efficient-fine-tuning.md) — Implements parameter-efficient fine-tuning using LoRA and QLoRA to reduce compute requirements. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [Retrieval Augmented Generation Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/retrieval-augmented-generation-pipelines.md) — Provides frameworks for designing retrieval-augmented generation (RAG) pipelines.
- [RLHF Alignment Algorithms](https://awesome-repositories.com/f/artificial-intelligence-ml/rlhf-alignment-algorithms.md) — Details RLHF alignment algorithms like PPO for matching model outputs to human preferences.
- [Autoregressive Text Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/sequence-generation/autoregressive-text-generation.md) — Includes study resources on autoregressive text generation for producing coherent sequences.
- [System Design Principles](https://awesome-repositories.com/f/artificial-intelligence-ml/system-design-principles.md) — Provides architectural strategies for building and scaling production-ready machine learning systems. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/README.md))
- [Transformer Architecture Analysis](https://awesome-repositories.com/f/artificial-intelligence-ml/transformer-architecture-analysis.md) — Analyzes transformer internals such as KV caching, positional encodings, and attention mechanisms. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [Data Collection and Labeling Strategies](https://awesome-repositories.com/f/artificial-intelligence-ml/data-collection-and-labeling-strategies.md) — Provides strategies for data ingestion and labeling using active learning and weak supervision. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))
- [Dataset Sampling Utilities](https://awesome-repositories.com/f/artificial-intelligence-ml/dataset-preparation-tools/dataset-sampling-utilities.md) — Covers dataset sampling utilities and techniques for creating representative subsets for ML training. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLC/ml-coding.md))
- [Feature Engineering](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-engineering.md) — Teaches techniques for transforming raw data into meaningful features and embeddings for ML models. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))
- [Generative Media Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-media-architectures.md) — Provides theoretical and implementation guides for generating high-dimensional media via autoregressive and diffusion models. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [Generative Model Evaluation](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-model-evaluation.md) — Implements evaluation techniques for generative outputs using retrieval triads and judge-based metrics. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [Machine Learning Evaluation](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-analysis/machine-learning-evaluation.md) — Explains how to evaluate model performance using precision, recall, and ROC curves. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [Evaluation & Validation](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-and-validation.md) — Explains how to select offline metrics and online testing frameworks to quantify model performance.
- [Model Selection and Validation](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-and-validation/model-selection-and-validation.md) — Includes methods for model selection, hyperparameter tuning, and validation to optimize performance. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))
- [Optimized Model Serving](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/serving-and-runtime/large-language-model-optimization/optimized-model-serving.md) — Details strategies for reducing cost and latency in LLM serving via KV caching and quantization. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))
- [Model Architecture](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/architecture-and-operations/model-architecture.md) — Covers the structural design of machine learning model components, including generators and rankers. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))
- [System Monitoring and Scaling](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-training-and-tuning/distributed-and-scaling-strategies/system-monitoring-and-scaling.md) — Includes guidelines on implementing distributed training and detecting data distribution shifts. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))
- [Multimodal Models](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-models.md) — Provides technical guidance on constructing neural network architectures that align multiple data types in a shared representation space. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [Performance Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/performance-metrics.md) — Provides guidance on selecting and calculating performance metrics for evaluating machine learning models. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))

### Part of an Awesome List

- [Gaussian Noise Diffusion](https://awesome-repositories.com/f/awesome-lists/ai/gaussian-noise-diffusion.md) — Provides theoretical fundamentals and implementation details for Gaussian noise diffusion and denoising.
- [KV Cache Management](https://awesome-repositories.com/f/awesome-lists/ai/kv-cache-management.md) — Offers optimization strategies for managing the KV cache to reduce LLM inference latency.
- [Deep Learning Study Guides](https://awesome-repositories.com/f/awesome-lists/learning/study-guides-and-portals/deep-learning-study-guides.md) — Acts as a technical reference for implementing transformers, diffusion models, and recurrent neural network architectures.
- [In Context Learning](https://awesome-repositories.com/f/awesome-lists/ai/in-context-learning.md) — Details few-shot prompting and chain-of-thought techniques to implement in-context learning. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [Model Optimization and Deployment](https://awesome-repositories.com/f/awesome-lists/ai/model-optimization-and-deployment.md) — Details strategies for model deployment, including A/B testing and canary releases in production. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))
- [Dimensionality Reduction](https://awesome-repositories.com/f/awesome-lists/data/dimensionality-reduction.md) — Covers dimensionality reduction techniques like PCA, ICA, and T-SNE for data simplification. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))
- [MLOps and Deployment](https://awesome-repositories.com/f/awesome-lists/devops/mlops-and-deployment.md) — Covers MLOps workflows for production deployment, including A/B testing and canary releases.

### DevOps & Infrastructure

- [LLM Inference Optimization](https://awesome-repositories.com/f/devops-infrastructure/ai-infrastructure/llm-inference-optimization.md) — Provides techniques for reducing LLM inference latency and memory via quantization and paged attention. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/ml-fundamental.md))

### Testing & Quality Assurance

- [LLM Evaluation](https://awesome-repositories.com/f/testing-quality-assurance/model-testing/llm-evaluation.md) — Provides methodologies for evaluating LLM performance using golden sets and automated judge-based metrics. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))

### Business & Productivity Software

- [Prediction Service Design](https://awesome-repositories.com/f/business-productivity-software/expert-directories/implementation-services/prediction-service-design.md) — Covers the design of serving layers using batch or online modes and model compression techniques. ([source](https://github.com/alirezadir/Machine-Learning-Interviews/blob/main/src/MLSD/ml-system-design.md))