MyMLNotebook is a personal AI/ML learning journal that documents my progression through machine learning concepts, practical experimentation, and hands-on model development.
This repository serves as both a learning log and a technical reference, capturing insights gained through continuous practice.
I created this repository to:
- Systematically track my learning journey in Artificial Intelligence and Machine Learning
- Experiment with algorithms, data preprocessing, and modeling techniques
- Consolidate concepts through implementation and reflection
- Build a reusable reference for future projects and revision
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Notes Conceptual explanations, observations, and reflections gathered while studying AI/ML topics.
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Code Snippets Reusable Python examples demonstrating syntax, patterns, and common ML workflows.
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Models Machine learning models I have implemented, including experiments with different algorithms and techniques.
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Datasets Datasets used for training, evaluation, and experimentation.
The repository evolves over time and may include:
- Supervised and unsupervised learning examples
- Data preprocessing and feature engineering techniques
- Model evaluation and comparison
- Experimentation with real-world datasets
This is a learning-focused repository. Code and notes may be iterative, exploratory, or experimental as concepts are refined over time.