Welcome to my machine learning learning repository!
This repo does not contain any standalone machine learning projects, but is focused on learning.
This is a collection of my learning journey and projects as I develop skills in machine learning and data science.
Contents
This repository contains the following learning resources:
Tutorials: Jupyter notebooks and Python scripts walking through machine learning concepts like regression, classification, clustering, and neural networks.
Mini Projects: Small example projects to practice specific ML skills and techniques.
ML Concepts: Notes, summaries, flashcards on machine learning theory and concepts like bias-variance tradeoff, cross-validation and regularization
Cheat Sheets: Quick reference cheat sheets for ML algorithms, Python data science and libraries.
Datasets: Small curated open datasets to practice ML modelling.
Roadmaps: Roadmaps and study plans for gaining ML skills.
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The following open source packages are used in this repo:
- Numpy
- Pandas
- Matplotlib
- Scikit-Learn
- Seaborn
- Sklearn
- SciPy
- StatsModels
- Keras
- TensorFlow
- PyTorch
- NLTK
- SpaCy
Juan Miguel López Piñero
