Chapter 1– category –

Review of Chapter 1 Hello! Over the past 29 sessions, we have covered the basics and applications of AI. This first chapter helped build a solid foundation for understanding AI and prepare us for the next steps. We explored key topics su...

Recap of Last Time and Today's Topic Hello! Last time, we learned about clustering, a technique for grouping data points based on their similarity. Clustering allows us to discover hidden patterns in the data. Today, we’ll explore one of...

Recap of Last Time and Today's Topic Hello! In the last session, we learned about Principal Component Analysis (PCA), a dimensionality reduction technique that simplifies data and improves model efficiency. Today, we will focus on cluste...

Recap of Last Time and Today's Topic Hello! Last time, we learned about dimensionality reduction, a method used to simplify datasets and improve model efficiency. Today, we will take a deeper dive into one of the most widely used dimensi...

Recap of Last Time and Today's Topic Hello! In the last session, we explored feature selection, which helps improve the quality of data used for training a model by selecting the most relevant features. Today, we will dive into dimension...

Recap of Last Time and Today's Topic Hello! In the last session, we discussed categorical variable encoding, a technique for converting text-based variables into numerical data. Proper encoding can significantly enhance a model's accurac...

Recap of Last Time and Today's Topic Hello! Last time, we learned about data standardization and normalization, which help ensure that a model can learn evenly from different features by aligning the data’s scale. Today, we will discuss ...

Recap of Last Time and Today's Topic Hello! In the last session, we learned how to detect and handle outliers in datasets. Properly addressing outliers improved the accuracy and reliability of our models. Today, we will cover an importan...

Recap of Last Time and Today's Topic Hello! In the last session, we learned how to handle missing data in datasets. Missing values are unavoidable in many cases, but by handling them appropriately, we can improve the accuracy of our mode...

Recap of Last Time and Today's Topic Hello! In the last session, we learned about data preprocessing—the steps needed to prepare data so that models can learn effectively. One key aspect of preprocessing is handling missing data in the d...

Quick Recap and Today’s Topic Welcome back! In our last session, we talked about cross-validation, which is a way to check how well a model performs by splitting the data into multiple parts. Today, we’re focusing on data preprocessing—a...

Recap of Last Time and Today's Topic Hello! In the last session, we explored evaluation metrics, which provide a way to objectively measure how well an AI model performs. Today, we will learn about cross-validation, a method that helps m...

Recap of Last Time and Today's Topic Hello! Last time, we learned about hyperparameters, which are crucial for optimizing model performance. Setting and tuning hyperparameters directly affects the learning process. Today, we’ll focus on ...

Recap of Last Time and Today's Topic Hello! In the last session, we explored the concepts of bias and variance, two factors that influence a model’s accuracy and generalization performance. By balancing these two, we can optimize the mod...

Recap of Last Time and Today's Topic Hello! In the last session, we learned about classification and regression, two major types of prediction problems in AI. Classification assigns data to categories, while regression predicts continuou...