data-science-learning-resources is a Research & Education tool that provides a curated collection of helpful data science and machine learning resources. It includes resources for programming, machine learning, and leadership & strategy, all personally vetted by the creator.
data-science-learning-resources is a comprehensive collection of curated learning materials for data science and machine learning. The repository, maintained by Bradley Boehmke, focuses on resources that the creator has personally read and found helpful, ensuring a high level of quality and relevance. It categorizes resources into key areas such as Programming (Python, R, Spark, Command Line, Containers, Functional Programming, Version Control, Code Packaging, Style Guide, Testing), Machine Learning (General, Unsupervised Modeling, A/B Testing, various algorithms like MARS, KNN, Random Forests, GBM, Deep Learning, Ensembles, NLP, Recommendation Systems, Tuning, Feature Engineering, Interpretability, AutoML, Benchmarking, Resampling, Productionalization, Model Monitoring), and Leadership & Strategy (Management & Leadership, Cloud Strategy, Product, Performance Reviews). This makes it an invaluable resource for anyone looking to deepen their understanding and skills in these domains.
Best used for
Ideal for students and professors who need to find high-quality, vetted learning materials for data science, machine learning, and related programming topics. Especially valuable for those seeking resources that have been personally reviewed and recommended by an experienced practitioner.
What types of resources are included in this collection?
The collection includes a wide array of resources such as books, online articles, tutorials, GitHub repositories, papers, and MOOCs. These cover programming languages like Python and R, various machine learning algorithms, deep learning, natural language processing, and even leadership and strategy topics relevant to data science.
How are the resources selected for inclusion?
All resources in this collection have been personally read and found helpful by the creator, Bradley Boehmke. This curation process ensures that the listed materials are of high quality and provide valuable insights for learning and professional development in data science and machine learning.
Is this resource suitable for beginners in data science?
While some introductory materials are included, many resources delve into intermediate and advanced topics across programming and machine learning. It's best suited for individuals with some foundational knowledge looking to deepen their expertise or explore specific advanced areas within data science.