What is Feature Engineering | Feature Engineering Techniques | Feature Engineering | Intellipaat
21In this video, we'll be exploring the concept of feature engineering in machine learning. Feature engineering is selecting and transforming raw data into a set of features suitable for use in a machine-learning model. Good feature engineering can be the key to building a highly accurate and efficient model, while poor feature engineering can lead to inaccurate results and wasted resources.
In this video, we'll cover the basics of feature engineering, including why it's essential, what makes a good feature, and how to go about selecting and transforming your data. We'll also discuss some common techniques for feature engineering, such as scaling, encoding, and feature extraction, and provide examples of how these techniques can be used in practice.
Whether you're just getting started in machine learning or looking to improve your existing models, understanding feature engineering is a crucial step in building high-quality models. So, if you're ready to take your machine-learning skills to the next level, be sure to watch this video and learn all about feature engineering!
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🔵 Following topics are covered in this session:
0:00 - Introduction to Feature Engineering
1:40 - Introduction to Feature Engineering
2:03 - Different Techniques for Feature Engineering
4:21 - Handling Continous Features
4:39 - Normalization
5:19 - Standardization
5:41 - Handling Categorical Features
7:26 - Feature Selection
7:59 - Common Methods For Feature Selection
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