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Explore the Machine Learning Interview Prep repository, your go-to for curated questions and answers tailored for data scientists. Elevate your skills, tackle technical interviews with confidence, and prepare for success in the dynamic field of machine learning!!

Home Page: https://towardsmachinelearning.org/

License: MIT License

interview-guide interview-preparation interview-questions machine-learning machine-learning-algorithms

machine-learning-interview-preparation's Introduction

Machine Learning Interview Preparation Guide

Welcome to the Machine Learning Interview Questions and Answers repository! This collection is designed to help you prepare for machine learning interviews by providing a comprehensive set of questions and detailed answers.

Table of Contents

  1. Introduction
  2. Getting Started
  3. Topics Covered
  4. Contributing
  5. License

Introduction

Machine learning interviews can be challenging, covering a wide range of topics such as algorithms, data preprocessing, model evaluation, and more. This repository aims to assist candidates in their preparation for machine learning interviews by compiling a diverse set of questions along with detailed explanations.

Getting Started

To get started, you can clone this repository to your local machine using the following command:

git clone https://github.com/Praveen76/Machine-Learning-Interview-preparation.git

Feel free to explore the questions and answers provided. You can use them to test your knowledge, practice answering questions, or enhance your understanding of key machine learning concepts.

Topics Covered

The questions in this repository cover a broad spectrum of machine learning topics, including but not limited to:

  • Supervised Learning: Regression, Classification, Decision Trees, Support Vector Machines, etc.
  • Unsupervised Learning: Clustering, Dimensionality Reduction, Association Rules, etc.
  • Neural Networks: Basics, Architectures, Training, etc.
  • Model Evaluation: Metrics, Cross-validation, Overfitting, etc.
  • Feature Engineering: Feature Scaling, Transformation, Selection, etc.
  • Data Preprocessing: Cleaning, Imputation, Encoding, etc.
  • Probability and Statistics: Bayes' Theorem, Probability Distributions, Hypothesis Testing, etc.

Feel free to explore the folders and dive into specific topics that interest you.

Contributing

Your contributions to this repository are highly welcome! If you have additional questions, better explanations, or new topics to cover, please feel free to open an issue or submit a pull request. For more details on how to contribute, please check the CONTRIBUTING.md file.

License

This repository is licensed under the MIT License, which means you are free to use, modify, and distribute the content as long as you provide attribution and include the same license in any derivative work.

Happy learning and best of luck with your machine learning interviews!

About Me:

Iโ€™m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.

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