Section: 3. Training Models (EN) | Artificial Intelligence and Machine Learning Fundamentals

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Section outline

Introduction

Welcome to the topic of Learning Models! This section delves into the various models and techniques used in machine learning to solve different types of problems. You will learn about linear regression, classification, decision trees, and the k-nearest neighbors algorithm, among others. We will discuss the characteristics, advantages, and disadvantages of each model, and the importance of model validation to ensure accuracy and reliability. Through practical exercises, you will apply these models to real datasets and interpret the results.
- Select activity 3.1 Linear Regression
  
  3.1 Linear Regression Page
  
  Students must
  
  View
- Select activity 3.2 Gradient Descent
  
  3.2 Gradient Descent Page
  
  Students must
  
  View
- Select activity 3.3 Polynomial regression
  
  3.3 Polynomial regression Page
  
  Students must
  
  View
- Select activity 3.4 Multiple linear regression
  
  3.4 Multiple linear regression Page
  
  Students must
  
  View
- Select activity 3.5 Classification, types of classification, and matrix of confusion
  
  3.5 Classification, types of classification, and matrix of confusion Page
  
  Students must
  
  View
- Select activity 3.6 Logistic regression
  
  3.6 Logistic regression Page
  
  Students must
  
  View
- Select activity 3.7 Decision tree
  
  3.7 Decision tree Page
  
  Students must
  
  View
- Select activity 3.8 K-Nearest Neighbor (kNN) Algorithm
  
  3.8 K-Nearest Neighbor (kNN) Algorithm Page
  
  Students must
  
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- Select activity 3.9 Hyperparameters
  
  3.9 Hyperparameters Page
  
  Students must
  
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- Select activity 3.10 Generalization, under-adaptation, and over-adaptation
  
  3.10 Generalization, under-adaptation, and over-adaptation Page
  
  Students must
  
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- Select activity 3.11 Validation, cross-validation
  
  3.11 Validation, cross-validation Page
  
  Students must
  
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- Select activity 3.12 Regularization
  
  3.12 Regularization Page
  
  Students must
  
  View
- Select activity Exercise 5.1: Linear regression
  
  Exercise 5.1: Linear regression Assignment
  
  Students must
  
  Make a submission
- Select activity Exercise 5.2: Gradient Descent
  
  Exercise 5.2: Gradient Descent Assignment
  
  Students must
  
  Make a submission
- Select activity Exercise 6: Logistic Regression
  
  Exercise 6: Logistic Regression Assignment
  
  Students must
  
  Make a submission
- Select activity Exercise 7: Decision tree
  
  Exercise 7: Decision tree Assignment
  
  Students must
  
  Make a submission
- Select activity Exercise 8: The k-nearest neighbor algorithm
  
  Exercise 8: The k-nearest neighbor algorithm Assignment
  
  Students must
  
  Make a submission
- Select activity Exercise 9: Validation
  
  Exercise 9: Validation Assignment
  
  Students must
  
  Make a submission

Section outline

Introduction