Machine Learning Course

Free hands-on and interactive course in Python, which starting from Data Science offers examples (in Google Colab) and explanation (in Twitter threads) on concepts and techniques of Machine Learning, Deep Learning and NLP.

Although it is not intended to have the formal rigor of a book, we tried to be as faithful as possible to the original algorithms and methods, only adding variants, when these were necessary for didactic purposes.

Quick Start

The best way to get the most out of this course is to carefully read each selected problem, try to think of a possible solution (language independent) and then look at the proposed Python code and try to reproduce it in your favorite IDE. If you already have knowledge of the Python language, then you can go directly to programming your solution and then compare it with the one proposed in the course.

If you want to play with these notebooks online without having to install any library or configure hardware, you can use the following service:

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Contents

1. Data Science
   • Correlation Coefficients: explanation | code
   • Feature Scaling: explanation | code
   • Entropy and Cross-Entropy: explanation | code
   • Data Visualization & Dimensionality Reduction: code
   • Linear Discriminant Analysis: code
2. Machine Learning
   • What is Machine Learning: explanation
   • Fundamentals: explanation
   • Tips for deploying ML models: explanation
   • Unsupervised Learning (UL)
     • Clustering: explanation
     • Hierarchical Clustering: explanation | code
     • DBSCAN Clustering: explanation | code
     • Clustering Validation: explanation | code
   • Supervised Learning (SL)
     • Methodology for regression problems: explanation
     • Linear Regression: code
     • Logistic Regression: code
     • Confusion Matrix: explanation | resource
     • ROC Curve: code
     • Overfitting and Regularization: explanation
3. Deep Learning
   • XOR NN Solution: explanation | code
   • Feed Forward NN: explanation | code
   • CNN to Classify Images: explanation | code
4. Natural Language Processing
   • Computational Linguistics vs NLP: explanation
   • Top NLP Libraries: explanation
   • Text Normalization: explanation | code
   • Split Sentences: explanation | code
   • Embeddings vs one-hot encoding: explanation
   • spaCy: explanation
   • Hugging Face example: code

Contributing and Feedback

Any kind of feedback/suggestions would be greatly appreciated (algorithm design, documentation, improvement ideas, spelling mistakes, etc…). If you want to make a contribution to the course you can do it through a PR.

Author

• Created by Andrés Segura-Tinoco
• Created on Mar 06, 2021

License

This project is licensed under the terms of the MIT license.