Janani Ravi | Duration: 16h | Video: H264 1280x720 | 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)
Courses in this pathA. Beginner
Learn how feature engineering fits into the machine learning workflow, and build your first features from numerical data.
A1. Preparing Data for Feature Engineering and Machine Learning (Janani Ravi, 2019)
A2. Building Features from Numeric Data (Janani Ravi, 2019)
B. Intermediate
Transform nominal data, such as names or categories, into features appropriate for machine learning, and apply techniques for simplifying large data sets.
B1. Building Features from Nominal Data (Janani Ravi, 2019)
B2. Reducing Complexity in Data (Janani Ravi, 2019)
C. Advanced
Extract features from text documents and images.
C1. Building Features from Text Data (Janani Ravi, 2019)
C2. Building Features from Image Data (Janani Ravi, 2019)
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