A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between.

Machine Learning interview prep guide

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Prepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!

A curated collection of deep learning programs, from foundational algorithms to cutting-edge architectures. Dive into hands-on implementations of KNN, PCA, DNNs, CNNs, and LSTMs — built with clarity, visualizations, and reusable templates. Perfect for learners, tinkerers, and interview preppers who want to master machine learning workflows the righ

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A comprehensive repository documenting my Machine Learning learning journey with detailed notes and practical code implementations. This repo covers fundamental ML concepts, algorithms, and hands-on coding in Python, NumPy, Pandas, Scikit-Learn, TensorFlow, and more. Perfect for learning, revision, and interview prep!

A curated collection of resources, templates, and guides to help students and professionals launch and advance their careers in AI and machine learning. Includes resume templates, portfolio ideas, interview prep, and learning roadmaps.

Machine learning interview questions

Computer vision, machine learning and programming interview prep

Machine learning core concepts. Great for data science and machine learning interview prep

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Repo of AI ML interview prep - coding machine learning/deep learning/AI from scratch and typical data structures.

Hi, I'm Rohan Diwakar — a B.Tech CS student exploring Java, React Native, Node.js, and Machine Learning through real-world projects; currently building hands-on applications, open to collaborating on open-source and hackathon projects, looking for guidance in backend scalability and interview prep, learning full-stack development and ML fundamental

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This repo contains seven parts: ML knowledge, ML Basic Algorithm, DL knowledge, DL Basic Algorithm, GenAI knowledge, interview preparation, RL knowledge

Some materials for ML/DS Interviews

My personal notes, cheatsheets and guide for review before Machine Learning or Data Science interviews.

Structured machine learning interview preparation notes

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Data Science, Machine Learning and Deep Learning Interview Prep PDF from iNeuron

This repository provides well-structured Q&A content on Data Analytics, Artificial Intelligence, Machine Learning, data pipelines, ETL processes, and analytics roles. Ideal for students, exam prep, and interview revision.

ByteHub is a learning platform for tech enthusiasts to build skills in foundational subjects like Computer Networks and Operating Systems, and explore trending fields like Machine Learning and Cloud Computing. Perfect for interview prep, ByteHub provides interactive tools for hands-on learning and upskilling.

Machine Learning Interview Prep

This Repo has Documents that help in preparing for Interviews in the ML/AI/DS Fields

This repo is to target a full time role in Machine Learning domain.

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List of resources for MLE/AS/DS interview preparation.