Learning AI from scratch– category –

Recap: Precision-Recall Curve (PR Curve) In the previous lesson, we discussed the PR Curve (Precision-Recall Curve), a graph that illustrates the relationship between Precision and Recall. The PR curve is particularly useful for evaluati...

Recap: ROC Curve and AUC In the previous lesson, we discussed the ROC Curve (Receiver Operating Characteristic curve) and AUC (Area Under the Curve). The ROC curve visually evaluates the performance of binary classification models by ill...

Recap: F1 Score In the previous lesson, we covered the F1 Score, which combines Precision and Recall through their harmonic mean. The F1 Score is essential for evaluating the balance between precision and recall, especially when there is...

Recap: Mean Squared Error (MSE) In the previous lesson, we covered Mean Squared Error (MSE), which calculates the average of the squared differences between predicted and actual values. MSE emphasizes larger errors, making it a useful me...

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...