1.1 Intro to Python | ||
1.2 Pandas | ||
1.3 Exercise 📝 Send responses | ||
1.4 Solution |
2.1 EDA theory | ||
2.2 EDA theory 2 | ||
2.3 Exercise |
3.1 Linear Regression | ||
3.2 Logistic Regression | ||
3.3 Logistic Regression NLP | ||
3.4 Regularization | ||
3.5 Polynomial regression |
4.1 EDA | ||
4.2 Decission Tree | ||
4.3 Random Forest | ||
4.4 Gradient Boosting | ||
4.5 Neural Network |
5.1 Dimensionality Reduction | ||
5.2 Clustering |
6.1 Beautiful Soup |
7.1 BOW + Logistic Regression | ||
7.2 TF-IDF, N-Grams | ||
7.3 Embeddings | ||
7.4 RNN with Keras |
8.1 TimeSeries with Prophet 1 | ||
8.2 TimeSeries with Prophet 2 | ||
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)