4T-Instructional Series in Machine Learning and Artificial Intelligence
$60.00
In stock
SKU
IA24-MT-60001-INT-4T
Overview
This course provides a detailed introduction to Decision Trees, a powerful and versatile technique in the field of machine learning. Participants will explore key concepts, classification and regression techniques, as well as their practical application on real-world datasets. Additionally, the exciting topic of code generation using large language models will be addressed.
Course Structure:
Unit 1: Decision Trees for Classification
- Decision Trees Characteristics
- Gini Impurity Score, Entropy Inpurity Score
- Training Algorithm, Hyperparameters, Computational Complexity, Model Sensitivity and Stability
- First Decision Tree Classification Example
- Detailed Calculation of the Gini Score
- Decision Boundaries, Estimation of Class Probabilities, Making Predictions
- Detailed Calculation of the Entropy Score
- Second Decision Tree Classification Example
- Underfitting, Overfitting, Tree Depth
- Decision Tree Classification Example using the Iris Data Set
- Decision Regions, Confusion Matrix
- Feature Importance, Grid Search
Unit 2: Decision Trees for Regression
- Model Characteristics, Regression Tree Models, Training Algorithm
- Regression Tree Example, Overfitting versus Underfitting
- Regression Tree Example (continues)
- Overfitting
- Model Regularization
- Hyperparameter Optimization via GridSearcCV, Model Regularization via GridSearchCV
Unit 3: Decision Tree Example
- The California Housing Data Set
- Instantiate Regression Tree, Use GridSearchCV, Evaluate Performance, Visualize Tree
- Instantiate Random Forest Regressor, Use GridSearchCV, Evaluate Performance
- Instantiate Random Forest Regressor using Hyperparameters found through GridSearcCV, Evaluate Performance
- Decision Tree Overview (assigned reading)
Unit 4: Code Generation using Large Language Models
- Getting Started with the PaLM API (Google), Text and Code Generation
- A Possible Prompt Structure, Build your Prompt, Generate Code, Run the Generated Code
- A Second Code Generation Trial using a Higher Temperature, Run the Generated Code
- Getting Started with OpenAI API, Code Generation using DaVinci Model and GPT-4
| Learning Objectives | Participants will explore key concepts, classification and regression techniques, as well as their practical application on real-world datasets. |
|---|---|
| Contact Hours | 4 Horas |
| CIAPR courses | CURSO TECHNICO |
| Instructor | Marvi Teixeira, PhD |
| Devices | Desktop, Tablet, Mobile |
| Language | Español |
IACET ACCREDITED PROVIDER
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Self Learning Solutions LLC is a company with more than 14 years of experience in this market. At Self Learning Solutions we are proud to have obtained the IACET accreditation for our organization, along with the approvals necessary to market our products throughout the United States. Self Learning Solutions is accredited by the International Association for Continuing Education and Training (IACET). Self Learning Solutions complies with the ANSI / IACET standard, which is recognized internationally as a standard of excellence in instructional practices. As a result of this accreditation, Self Learning Solutions is accredited to issue the CEU IACET. |
SLSTECH System Requirements
To run our system effectively you should, as a minimum, use the system components listed on this page. If you do not, the system may still work but some functionality may be lost. Workplace IT environments' internal configurations can also restrict the functionality of our system. Access to content may be affected, as may the possibility of uploading files. File size limitations may also apply. Workplaces may also have older versions of software, and our system may not perform well with these.
Operating system
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Recommended: Windows 7, 10, Mac OSX Sierra, iPad IOS10
Internet speed
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Use a broadband connection (256 Kbit/sec or faster—this will ensure that you can view videos and online presentations) through USB wireless modem, ADSL, T1/T2, fibre optic or cable.
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Dial-up access will be significantly slower, and we do not recommend it for using our system.
Internet browsers
Compatible browsers include:
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Google Chrome 32 bit version 50 or later (recommended for optimal compatibility, this has been thoroughly tested on Windows)
Safari 10 or later (recommended for optimal compatibility, this has been thoroughly tested on Mac)
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MS Internet Explorer is not recommended
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Pop-ups (in both Internet browser and security software)
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We recommend that you use the latest version of Adobe Acrobat Reader.
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