Learning AI from scratch– category –

Recap and Today's Topic Hello! Last time, we learned about boosting, an ensemble learning technique that sequentially trains models and corrects errors to improve overall performance. Today, we will focus on XGBoost—an extremely high-per...

Recap and Today's Topic Hello! Last time, we explored ensemble learning, a technique that combines multiple models to improve accuracy. Ensemble learning enhances overall predictive power by compensating for the weaknesses of individual ...

Recap of the Previous Lesson and Today’s Topic Hello! In our previous session, we explored Bagging, an ensemble learning method that uses data resampling to train multiple models in parallel, combining their results to make stable predic...

Recap of the Previous Lesson and Today’s Topic Hello! In the last session, we explored Naive Bayes Classification, a simple and fast algorithm that is widely used in spam filtering and text classification. Today, we will discuss Ensemble...

Recap of the Previous Lesson and Today’s Topic Hello! In the last session, we explored k-Nearest Neighbors (k-NN), a simple yet effective algorithm that makes predictions based on the proximity of nearby data points. Today, we will cover...

Recap of the Previous Lesson and Today’s Topic Hello! In the previous session, we discussed Support Vector Machines (SVM), a powerful algorithm for classifying data by finding optimal boundaries between classes. Today, we will learn abou...

Recap of the Previous Lesson and Today’s Topic Hello! In the last session, we learned about Gradient Boosting, an ensemble learning technique that builds powerful models by gradually correcting errors. Today, we’ll dive into another powe...

Recap of the Previous Lesson and Today’s Topic Hello! In our previous session, we learned about Random Forest, an ensemble learning technique that combines multiple decision trees to compensate for the weaknesses of individual trees, cre...

Recap of the Previous Lesson and Today’s Topic Hello! In the last session, we learned about the decision tree algorithm. This simple yet powerful algorithm is widely used across various fields for tasks like classification and prediction...

Recap of Last Time and Today's Topic In the last session, we learned about logistic regression, which is used for binary classification problems. It helps predict outcomes such as whether a customer will make a purchase or whether an ema...

Recap of Last Time and Today's Topic Hello! In the last session, we explored linear regression, a fundamental machine learning technique used for predicting continuous values. Today, we’ll focus on logistic regression, which is designed ...

Introduction In Chapter 2, we are focusing on learning the key algorithms commonly used in machine learning. In this session, we will start with linear regression, a fundamental model for predicting numerical values based on data. Despit...

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...