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Guidelines for how to learn about machine learning

This is a guideline generated by Llama 2 for learning about machine learning, for my own interest and may of of use to others. The rest of this page was generated by an LLM

This is a comprehensive guide that covers the basics of machine learning, including data preprocessing, feature selection and engineering, supervised and unsupervised learning, neural networks, model evaluation and selection, hyperparameter tuning, ensemble methods, model deployment and maintenance, and advanced topics in machine learning. This guide is designed to provide a solid foundation in machine learning and help you build your own models from scratch. Even most of this paragraph is generated by Llama 2!

Introduction to Machine Learning:

Data Preprocessing:

Feature Selection and Engineering:

Supervised Learning:

Unsupervised Learning:

Neural Networks and Deep Learning:

Model Evaluation and Selection:

Hyperparameter Tuning:

Ensemble Methods:

Model Deployment and Maintenance:

Advanced Topics in Machine Learning:

By following this guide, you'll have a solid foundation in machine learning and be able to build your own models from scratch. Good luck!

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