SKU/Artículo: AMZ-B0GKG7QR1V
Feature Engineering for ML : Transforming Raw Data into Powerful Inputs for Models
Format:
Kindle
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Fuera de stock
Fuera de stock
Peso con empaque:
0.76 kg
0.76 kg
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Sí
Sí
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Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- Algorithms are a Commodity. Data is the Asset.You can download the latest State-of-the-Art model with two lines of code. But if you feed it garbage, you will get garbage results. In modern Data Science, the difference between a 70% accuracy model and a 95% accuracy model isn't the algorithm—it's the features.Feature Engineering for ML is the definitive guide to the most critical step in the machine learning pipeline.This book moves beyond simple data cleaning to teach you the art and science of Feature Extraction and Feature Selection. You will learn how to transform raw, messy, and unstructured data into rich, informative signals that your models can actually learn from. Whether you are working with text, dates, numbers, or categories, this guide provides the toolkit to unlock the hidden patterns in your dataset.Inside, you will engineer better performance:Handle the Mess: Stop dropping rows. Learn advanced Imputation techniques (KNN, MICE) to handle missing values without introducing bias.Encode Reality: Move beyond One-Hot Encoding. Master Target Encoding, Frequency Encoding, and Embeddings to handle high-cardinality categorical variables.Tame the Numbers: Apply Log Transforms, Binning, and Scaling (MinMax vs. StandardScaler) to make your numerical data friendly to neural networks and linear models.Curse of Dimensionality: Use PCA (Principal Component Analysis) and t-SNE to reduce noise and speed up training without losing critical information.Time & Text: Extract powerful features from timestamps (cyclical encoding) and raw text (TF-IDF, Bag of Words) to give your model context.A model is only as smart as the data you give it. Make your data brilliant.Scroll up, click "Buy Now," and turn your raw data into gold!
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