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Feature Engineering

Janani Ravi

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'll 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 clusteringmodel stacking)

Image processing techniques (feature extraction)

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What's inside

Five courses

Preparing Data for Feature Engineering and Machine Learning

(3 hours)

This course covers feature engineering techniques to improve machine learning models, including feature selection and extraction techniques.

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Building Features from Numeric Data

(2 hours)

This course covers data preprocessing techniques and transforms available in scikit-learn, allowing the construction of highly optimized features that are scaled, normalized and transformed in mathematically sound ways to fully harness the power of machine learning techniques.

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Reducing Complexity in Data

(3 hours)

This course covers techniques to simplify data for supervised machine learning, from feature selection to clustering using deep neural networks.

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Building Features from Text Data

(2 hours)

This course covers extracting information from text documents and constructing classification models. Topics include feature vectorization, locality-sensitive hashing, stopword removal, and lemmatization.

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Building Features from Image Data

(2 hours)

This course covers pre-processing images to maximize the efficacy of image processing algorithms, as well as implementing feature extraction, dimensionality reduction, and latent factor identification.

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Time

13 hours

Via

Pluralsight

Institution

Instructor

Janani Ravi

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