Bloque 1: Introducción
Tools
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| 🐍 Python |
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| 🔢 Numpy |
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| 🐼 Pandas |
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Introducción al Machine Learning
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| Definir el problema y métrica | |
| Obtener datos de entrenamiento y validación | |
| Preprocesar datos | |
| Seleccionar modelo | |
| Optimización de hiperparámetros | |
| Metrics |
Bloque 2: Modelos de ML/DL
Tree-based models
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| 🌳 Decision Tree (CART, C4.5, M5) |
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| 🌳 Random Forest & Extra Trees |
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| 🌳 Gradient Boosting (XGBoost, LGBM, CatBoot) |
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Linear models
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| Linear Regression |
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| Logistic Regression |
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| Regularization (Ridge, Lasso, ElasticNet) |
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| Polynomial Regression |
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| Generalized Additive Model (GAM) |
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Neural Networks
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| Multi Layer Perceptron (MLP) |
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| Convolutional Neural Network (CNN, ResNet) | |
| Recurrent Neural Network (RNN, LSTM, GRU) | |
| Transformer (BERT, GPT, TabNet) | |
| 🔧 Hyperparameters | |
| 🥪 Layers Glossary |
Ensembling
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| Weighted average, Majority vote,... |
Bloque 3: Aplicaciones reales de ML/DL
Tabular
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| 🚗 Precio de coches de 2a mano |
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Time Series
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| Prophet (Walmart) |
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| Prophet (Medium) |
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| TSFresh + Sklearn |
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Collaborative filtering
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Natural Language Processing (NLP)
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| Bag of Words + LogRegr with Sklearn |
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| TFIDF + LogRegr with Sklearn |
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🔈 Audio
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| 🔈 Mel Spectogram |
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| 🔈 Audio classification (with data aug) |
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🎨 Image generation
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| ✂️ Semantic Segmentatition |
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| ✂️👩💼 Binary Segmentation (Portrait mode) |
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| 🔲✂️ Instance Segmentatition | |
| GANs | |
| 🖼️🪄 Image enhacement | |
| 🦯 Depth Segmentation | |
| 📃📄 Document unwarp |
🎥 Video
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| 〰️ Object Tracking | |
| 〰️ Optical Flow | |
| Background Removal | |
| Product Placement |