Feature Engineering and Selection | AIML End-to-End Session 65
Ready to dive deep into the world of
Artificial Intelligence
Machine Learning (AIML)?
Welcome to Session 65 of our End-to-End AIML series! In this session, we dive into one of the most crucial aspects of machine learning: Feature Engineering and Selection. Proper feature engineering and selection can significantly boost the performance of your models by highlighting the most relevant data and discarding unnecessary noise.
What You'll Learn:
Introduction to Feature Engineering: Learn the basics of creating new features from raw data to enhance model performance, including techniques like normalization, scaling, and encoding categorical variables.
Feature Transformation: Explore advanced methods like log transformations, polynomial features, and interaction terms that help to capture the true nature of the data.
Handling Missing Values: Discover strategies to deal with missing data, including imputation techniques and how missing data can impact model training.
Feature Selection Techniques: Learn how to select the most important features using methods such as:
Correlation Analysis
Recursive Feature Elimination (RFE)
LASSO and Ridge Regression
Tree-based Methods (e.g., Random Forests and Gradient Boosting)
Dimensionality Reduction: Understand the importance of reducing the number of features to improve model efficiency and interpretability, using methods like PCA and LDA.
Impact on Model Performance: Analyze real-world examples to see how effective feature engineering and selection can improve accuracy and reduce overfitting.
Best Practices for Feature Engineering: Get practical insights into how and when to apply feature engineering techniques for different types of data, such as text, time series, and images.
By the end of this session, you'll have a strong understanding of how to engineer and select the right features to build robust and high-performing machine learning models.
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