Welcome to the ML Guru content guide. I appreciate assistance from the consortium and community members for content creation and upkeep.
The guides on this repository are meant to assist you in preparing for the job interview for the position of DS\ML Engineer. Please feel free to reach out to me on LinkedIn (mrvlad) with any questions.
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- Theoretical questions: data_science_questions.md (linear models, trees, neural networks and others)
- Best Public Datasets for Machine Learning in 2023
- Online Retail II
- Titanic - Machine Learning from Disaster
- Machine Learning Glossary
- One or more version of the data science glossary
- Fundamental book: An Introduction to Statistical Learning
- Choosing the right estimator
- Visualizing K-Means Clustering
- A Gentle Introduction to Random Forests, Ensembles, and Performance Metrics in a Commercial System
- Introduction to Support Vector Machines
- Principal Component Analysis
- Book: A tutorial on Principal Components Analysis
- Ordinary Least Squares Regression
- Shannon entropy calculator
- StatQuest with Josh Starmer
- SVM with polynomial kernel visualization
- Why Use Kalman Filters? | Understanding Kalman Filters
This work is licensed under a Creative Commons Attribution 4.0 International License.