Janani Ravi | Duration: 16h | Video: H264 1280×720 | Audio: AAC 44,1 kHz 2ch | 2,76 GB | Language: English
Feature engineering is the process of using domain knowledge and insight into data to define features that enable machine learning algorithms to work successfully. Feature engineering is a fundamental part of the data preparation workflow for machine learning solutions.
What you will learn
• Qualities of effective features and how to assess them
• Numeric techniques (quantization binning, binarization, transforms, scaling, normalization)
• Text techniques (bag-of-x, filtering, n-grams, phrase detection)
• Categorical data techniques (one-hot encoding, hashing, bin counting, etc)
• Dimensionality reduction (PCA)
• Nonlinear featurization (K-means clustering model stacking)
• Image processing techniques (feature extraction)
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