| 1.1 Intro to Python |  |
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| 1.2 Pandas | |
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| 1.3 Exercise 📝 Send responses | |
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| 1.4 Solution | |
| 2.1 EDA theory | |
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| 2.2 EDA theory 2 | |
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| 2.3 Exercise | |
| 3.1 Linear Regression | |
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| 3.2 Logistic Regression | |
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| 3.3 Logistic Regression NLP | |
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| 3.4 Regularization | |
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| 3.5 Polynomial regression | |
| 4.1 EDA | |
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| 4.2 Decission Tree | |
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| 4.3 Random Forest | |
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| 4.4 Gradient Boosting | |
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| 4.5 Neural Network | |
| 5.1 Dimensionality Reduction | |
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| 5.2 Clustering | |
| 6.1 Beautiful Soup | |
| 7.1 BOW + Logistic Regression | |
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| 7.2 TF-IDF, N-Grams | |
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| 7.3 Embeddings | |
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| 7.4 RNN with Keras | |
| 8.1 TimeSeries with Prophet 1 | |
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| 8.2 TimeSeries with Prophet 2 | |
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| 8.3 Ejercicio en Kaggle | |
| 9.1 PY4PY package | |
| 9.2 Exercise | |
| 10.1 Efficient Pandas (reduce memory...) | |
| 10.2 H20 datatable | |
| 10.3 Distributed ML: Pyspark | |
| 10.4 GPU ML: RAPIDS (cuDF & cuML) | |
| 10.5 Exercise |
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Imagen
- Clasificanción: (ej: clases de perros)
- Localización: (bounding boxes)
- Segementacion: (pixel level)
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Sonido
- Clasificación
- Clasificación temporal
- Separar fuentes de sonidos
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Puesta de modelos en producción
- Poner en web: Creación de un API
- Poner en sensor: Puesta en RaspberryPi
- Mlcourse.ai (advanced)
- Kaggle learn (easy)
- Fast.ai ML (easy)
- Deep Learning (advanced)