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

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

Content

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

Machine Learning

What is Machine Learning: explanation
Fundamentals: explanation
Unsupervised Learning (UL)
- Clustering: explanation
- Hierarchical Clustering: explanation | code
- DBSCAN Clustering: explanation | code
- Clustering Validation: explanation | code
Supervised Learning (SL)
- Linear Regression: code
- Logistic Regression: code
- Confusion Matrix: explanation
- ROC Curve: code
- Overfitting and Regularization: explanation

Deep Learning

XOR NN Solution: explanation | code
Feed Forward NN: explanation | code
CNN to Classify Images: explanation | code

Natural Language Processing

Computational Linguistics vs NLP: explanation
Top NLP Libraries: explanation
Text Normalization: explanation | code
spaCy: explanation

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
Updated on Jul 03, 2021

License