This repository compiles essential links and resources for machine learning interviews, targeting engineers and researchers preparing for technical assessments. It offers a structured overview of key concepts, algorithms, and practical considerations in ML, aiming to streamline the revision process.

How It Works

The repository acts as a curated index of external articles, blog posts, and documentation, organized by topic. It covers feature engineering, algorithm fundamentals (evaluation metrics, regularization, loss functions, optimization), specific ML algorithms (linear models, trees, ensemble methods), NLP techniques (word embeddings, Transformers), and recommendation systems. The emphasis is on providing pointers to detailed explanations and practical examples rather than reproducing content.

Quick Start & Requirements

• Access to the internet to follow the provided links.
• Familiarity with machine learning concepts is assumed.
• No installation or specific software is required to browse the resources.

Highlighted Details

• Comprehensive coverage of feature engineering, including preprocessing, expression, and selection methods.
• Detailed explanations of core algorithms like SVM, GBDT, XGBoost, and LightGBM, with links to in-depth tutorials.
• Extensive NLP section covering word2vec, LSTMs, Transformers, BERT, and XLNet.
• Resources for recommendation systems and computational advertising.
• Curated list of recommended books, blogs, and interview question summaries.

Maintenance & Community

The repository appears to be a personal compilation, with no explicit mention of active maintenance, contributors, or community channels.

Licensing & Compatibility

The repository itself does not contain code and is a collection of links. The licensing of the linked external resources would vary.

Limitations & Caveats

This repository is a pointer to external resources and does not host original content or code. The quality and accuracy of the linked materials are dependent on their original sources. Some links may become outdated over time.