Summary
The video delves into the crucial role of data pre-processing in ensuring the accuracy of machine learning algorithms. It discusses the importance of scaling data to bring features onto the same scale, showcasing techniques like Min-Max Scaling and Normalization. The speaker also covers handling categorical features, missing values, and creating new features, emphasizing the significance of feature selection. Additionally, the video explains normalization techniques for high-dimensional data and strategies for dealing with sparse and non-normally distributed data, making it an informative resource for enhancing the performance of machine learning models.
Introduction to Data Pre-processing
Introduction to the importance of data pre-processing in machine learning, discussing the assumptions made by machine learning algorithms and the challenges posed by real-world data.
Scaling Data
Explanation of scaling data to ensure features are on the same scale and how different scaling techniques like Min-Max Scaling, Robust Scaling, and Normalization can be applied.
Feature Transformation
Discussion on transforming features including handling categorical features, dealing with missing values, creating new features, and the importance of feature selection.
Normalization Techniques
Explanation of normalization techniques for high-dimensional data, such as cosine similarity and vector representation models, to compute meaningful distances.
Handling Sparse Data
Explanation of handling sparse data using techniques like MaxAbsScaler to preserve sparseness while scaling the data. Useful for recommender systems and scenarios with a large number of zero values.
Handling Non-Normal Data
Explanation of handling non-normally distributed data by using techniques like Box-Cox Transformation to normalize the data effectively.
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