データ処理と特徴量エンジニアリング（121～150）– モデルの性能を最大限に引き出すためのデータ処理技術を学びます。 –

Recap: Network Data Analysis In the previous lesson, we explored network data analysis, learning how to use graph data composed of nodes and edges to visualize and analyze relationships across various domains, including social networks a...

Recap: Real-Time Data Processing In the previous lesson, we explored how to efficiently process streaming data using frameworks and time window techniques. This time, we will focus on data visualization tools and explain how to use them ...

Recap: Utilizing Data Visualization Tools In the previous lesson, we discussed popular data visualization libraries like Matplotlib, Seaborn, and Plotly. These tools help present data in a visual format, making patterns and trends easier...

Recap: Log Data Analysis In the previous lesson, we learned how to analyze log data generated by systems and applications, using it for performance monitoring, troubleshooting, and security enhancement. Today, we will explore network dat...

Recap: Evaluating Data Quality In the previous lesson, we discussed methods for assessing and improving data quality using criteria like accuracy, completeness, consistency, and timeliness. We learned that ensuring data reliability throu...

Recap: Data Security and Privacy In the previous lesson, we explored strategies for ensuring data security and privacy in the cloud, such as encryption, access control, and monitoring logs. We also discussed the importance of protecting ...

Recap: Using Cloud Services In the previous lesson, we explored how to utilize major cloud platforms like AWS, GCP, and Azure for large-scale data processing. While cloud computing offers convenience, ensuring data security and privacy r...

Recap: The Basics of Apache Spark In the previous lesson, we learned about Apache Spark, a powerful tool for high-speed, in-memory processing and distributed data handling, making it a widely used tool for big data. Today, we’ll discuss ...

Recap: Handling Big Data In the previous lesson, we discussed distributed processing frameworks like Apache Hadoop and Apache Spark for managing large-scale data efficiently. These frameworks distribute data across multiple nodes, enabli...

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...