Learning AI from scratch– category –
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Chapter 2
Lesson42:XGBoost
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... -
Chapter 2
Lesson40:What is Bagging? (Bootstrap Aggregating)
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 ... -
Chapter 2
Lesson 41: What is Boosting?
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... -
Chapter 2
Lesson 39: What is Ensemble Learning?
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... -
Chapter 2
Lesson 38: What is Naive Bayes Classification?
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... -
Chapter 2
Lesson 37: What is k-Nearest Neighbors (k-NN)?
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... -
Chapter 2
Lesson 36: What is a Support Vector Machine (SVM)?
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... -
Chapter 2
Lesson 35: What is Gradient Boosting?
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... -
Chapter 2
Lesson 34: What is Random Forest?
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... -
Chapter 7
Decision Tree Algorithm (Learning AI from scratch : Part 33)
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... -
Chapter 2
Logistic Regression (Learning AI from scratch : Part 32)
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 ... -
Chapter 2
Linear Regression (Learning AI from scratch : Part 31)
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... -
Chapter 1
Chapter 1 Summary and Comprehension Check (Learning AI from scratch : Part 30)
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... -
Chapter 1
k-Means Method (Learning AI from scratch : Part 29)
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... -
Chapter 1
Clustering (Learning AI from scratch : Part 28)
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...
