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Recap: Database Integration In the previous lesson, we learned how to integrate databases using SQL to efficiently retrieve data. SQL is a powerful tool for managing and querying structured data, but as data volumes increase, processing ...

Recap: Building Data Pipelines In the previous lesson, we discussed Data Pipelines, covering how to automate the entire workflow from data collection, preprocessing, model training, to evaluation. By automating these steps, data processi...

Recap: Automating Feature Engineering In the previous lesson, we explored Automating Feature Engineering using tools like FeatureTools to efficiently generate new features from data. By automating feature engineering, we save time and im...

Recap: Audio Data Preprocessing In the previous lesson, we covered audio data preprocessing, focusing on techniques like spectrograms and MFCCs (Mel-frequency cepstral coefficients). Spectrograms visualize the frequency components of aud...

Recap: Preprocessing Image Data In the previous lesson, we covered preprocessing methods for image data, focusing on resizing, normalization, and data augmentation. These methods help standardize image data, enabling machine learning mod...

Recap: Time Series Data Preprocessing In the previous lesson, we explored time series data preprocessing using lag features and moving averages. Lag features leverage past data to predict future values, while moving averages smooth short...

Recap: Methods for Imputing Missing Values In the previous lesson, we discussed methods for handling missing values using the mean, median, and mode. We explained when and how to apply each method to mitigate the negative impact of missi...

Recap: SMOTE for Over-Sampling In the previous lesson, we explored SMOTE (Synthetic Minority Over-sampling Technique), an effective method for addressing data imbalance by generating synthetic data for the minority class. SMOTE helps bal...

Recap: Addressing Data Imbalance In the previous lesson, we discussed various sampling techniques for handling imbalanced data in classification tasks. We covered over-sampling to increase the minority class data and under-sampling to re...

Recap: Applications of Dimensionality Reduction In the previous lesson, we learned about dimensionality reduction techniques like t-SNE and UMAP, which transform high-dimensional data into lower dimensions for better visualization and im...

Recap: Correlation Analysis In the previous lesson, we explored Correlation Analysis, a method used to measure the relationships between features and the target variable. By examining correlation coefficients and visualizing data through...

Recap: Feature Selection Techniques In the previous lesson, we covered Feature Selection Techniques, which involve removing unnecessary features to improve model performance. We discussed three main approaches: Filter Methods, Wrapper Me...

Recap: Feature Engineering In the previous lesson, we learned about Feature Engineering, which involves creating new features from data to improve model performance. We explored various techniques, such as mathematical operations, encodi...

Recap: Scaling Numerical Data In the previous lesson, we learned about Min-Max Scaling and Standardization. These techniques help adjust the range and variability of data, enhancing the efficiency and performance of machine learning mode...

Recap: Preprocessing Text Data In the previous lesson, we explored tokenization, stemming, and lemmatization—techniques for preprocessing text data. These methods transform natural language data into a format suitable for machine learnin...