Machine learning and applications

Learn about basic Machine learning algorithms and how to build Machine learning models

Beginner 0(0 Ratings) 0 Students enrolled English
Created by Nam Phạm Tuấn
Last updated Fri, 16-Aug-2024
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Course overview

Exercise

Complete optional exercises and ask for feedback from other students
Curriculum for this course
36 Lessons 00:00:00 Hours
Introduction
4 Lessons 00:00:00 Hours
  • Introduction
    .
  • What is machine learning?
    .
  • CHEAP software
    .
  • Univariate linear regression
    .
Linnear regression model
4 Lessons 00:00:00 Hours
  • Multivariable linear regression
    .
  • Practice linear regression on excel
    .
  • Practice linear regression on CHEAP software
    .
  • Sample code for the lecture
    .
Methods of Building machine learning models
4 Lessons 00:00:00 Hours
  • Overfitting phenomenon is machine learning
    .
  • Method of selection and model building - cross-validation
    .
  • Ridge Regression and LASSO . Regression
    .
  • Sample code for the lecture
    .
logistic rregression model
8 Lessons 00:00:00 Hours
  • (Optional) Maximum likelihood estimation method
    .
  • logistic rregression
    .
  • Model estimation and illustration on excel
    .
  • Model testing: Measuring forecast error
    .
  • Model testing: ROC curve, index AUC
    .
  • Practice Logistic Regression with CHEAP software
    .
  • Compare Linear Regression and Logistic Regression
    .
  • Sample code for the lecture
    .
The decision tree model
6 Lessons 00:00:00 Hours
  • the decision tree model
    .
  • Modeling and forecasting
    .
  • Model Estimation Algorithm
    .
  • Model Bagging and Random Forest
    .
  • Practice decision tree model on CHEAP software
    .
  • Sample code for the lecture
    .
Nearest Neighborhood Model
3 Lessons 00:00:00 Hours
  • Nearest Neighborhood Model
    .
  • Practice k-NN model on CHEAP software
    .
  • Sample code for the lecture
    .
Practice SupervisedLearning - Titanic Data - Survival Prediction
1 Lessons 00:00:00 Hours
  • Titanic data forecast
    .
unsupervised learning : Hierarchical clustering, k - means . clustering
6 Lessons 00:00:00 Hours
  • Supervised learning and unsupervised learning
    .
  • Hierarchical clustering
    .
  • K-means . clustering
    .
  • Practice hierarchical clustering with CHEAP software
    .
  • Practice K-means clustering with CHEAP software
    .
  • Sample code for the lecture
    .
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Nam Phạm Tuấn

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