Covers: theory of Tabular Data Preprocessing
Estimated time needed to finish: 2 minutes
Questions this item addresses:
- Can you summarize this recipe?
How to use this item?
Watch until 1:50 (extra).
Author(s) / creator(s) / reference(s)
Devinder Sarai
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Tabular Data Preprocessing for Continuous Data
Contributors
- Objectives
- You will learn two methods to preprocess continuous tabular data.
- Potential Use Cases
- Time-series forecasting, anomaly detection, power generation
- Who is This For ?
- BEGINNERData Scientists new to Tabular Data/Time-Series Preprocessing.
Click on each of the following annotated items to see details.
VIDEO 1. Introduction to Feature Normalization on Continuous Tabular Data
- Why use normalization or standardization?
- When do we perform feature normalization? When don't we?
8 minutes
ARTICLE 2. Understanding the Difference Between Normalization & Standardization
- What is Normalization?
- What is Standardization?
- Normalize or Standardize?
- How do we implement feature scaling in Python?
5 minutes
VIDEO 3. Using Normalization to Reconcile Financial Data
- What's a real-life example of using feature scaling?
4 minutes
LIBRARY 4. Preprocessing Data for Tabular Built-In Algorithms
- How to preprocess tabular data?
6 minutes
LIVE_SESSION 5. AISC Tabular Data Preprocessing Presentation
- Can you summarize this recipe?
2 minutes
Concepts Covered
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