Class Projects
Students will build, test, and publish their own projects in Processing Java.
CODING PLATFORM
The KTCoder all-in-one coding platform supports our interactive online classes, our specialized curriculum, and our students’ passion for learning.
STUDENT HELP HOURS
Help hours are led by our highly qualified teaching assistants. It is an easy and free way to get immediate feedback on your code.
PROGRESS REPORTS
KTBYTE will e-mail parents with behavior and grade progess reports.
COMPLETION CERTIFICATES
Students can request a certificate of completion once they finish each course.
Class Description:
This course introduces students to real-world data science and artificial intelligence through Python programming and hands-on projects. Students will explore industry-relevant tools such as Jupyter Notebooks and Kaggle, the world's largest platform for data science competitions. With guidance, they’ll learn how to build their own machine learning models—from simple decision trees to introductory neural networks—and apply them to real datasets.
By the end of the course, students will understand how AI can be used to analyze patterns, make predictions, and solve meaningful problems. Whether working on individual projects or submitting as a team to Kaggle’s beginner competitions, students will gain both technical and soft skills such as data literacy, logical thinking, and teamwork.
This is an excellent introduction for students interested in data science, AI, or competitive coding.
Prerequisites
Python 2, Core4a or Instructor Permission
Recommended for Grades 6 and up
Class Description:
This course introduces students to real-world data science and artificial intelligence through Python programming and hands-on projects. Students will explore industry-relevant tools such as Jupyter Notebooks and Kaggle, the world's largest platform for data science competitions. With guidance, they’ll learn how to build their own machine learning models—from simple decision trees to introductory neural networks—and apply them to real datasets.
By the end of the course, students will understand how AI can be used to analyze patterns, make predictions, and solve meaningful problems. Whether working on individual projects or submitting as a team to Kaggle’s beginner competitions, students will gain both technical and soft skills such as data literacy, logical thinking, and teamwork.
This is an excellent introduction for students interested in data science, AI, or competitive coding.
Prerequisites:
Python 2, Core4a or Instructor Permission
Recommended for Grades 6 and up
Related Classes
Syllabus:
Python Overview
Review functions, boolean logic, data structures, loops and lists
Intro to Pandas
Introduce Jupyter Notebook and Pandas library
Intro to Machine Learning Models
Learn about how basic models work, model validation, and error functions
Building a Decision Tree
Learn about the Decision Tree model and how it can be used to solve machine learning problems.
Model Evaluation and Validation
Learn about other simple models, including logistic regression and ensemble models, such as random forest and XGBoost.
First Kaggle Competition
Today we'll try the different models we learned in a competition, and see who can get the highest score!
Neural Networks using TensorFlow
Intro to deep learning methods with neural networks.
Natural Language Processing
Apply a neural network in a Natural Language Processing competition.
Neural Networks and Image Recognition
Today we'll learn about classifying images with neural networks, and cover the important topic of ethics in AI.
Final Project Introduction
<p>Students will choose a dataset from Kaggle about a topic that they care about.</p><p>They will then independently create a model to complete their goal (feel free to use the internet!)</p><p>Students will also create a presentation, i.e. google slides, explaining why they chose their topic and their methods used.</p>
Continue Final Projects
<p>Students will choose a dataset from Kaggle about a topic that they care about.</p><p>They will then independently create a model to complete their goal (feel free to use the internet!)</p><p>Students will also create a presentation, i.e. google slides, explaining why they chose their topic and their methods used.</p>
Final Project Presentations
<p>Share your final project to the class!</p>
Syllabus
Python Overview
Review functions, boolean logic, data structures, loops and lists
Intro to Pandas
Introduce Jupyter Notebook and Pandas library
Intro to Machine Learning Models
Learn about how basic models work, model validation, and error functions
Building a Decision Tree
Learn about the Decision Tree model and how it can be used to solve machine learning problems.
Model Evaluation and Validation
Learn about other simple models, including logistic regression and ensemble models, such as random forest and XGBoost.
First Kaggle Competition
Today we'll try the different models we learned in a competition, and see who can get the highest score!
Neural Networks using TensorFlow
Intro to deep learning methods with neural networks.
Natural Language Processing
Apply a neural network in a Natural Language Processing competition.
Neural Networks and Image Recognition
Today we'll learn about classifying images with neural networks, and cover the important topic of ethics in AI.
Final Project Introduction
<p>Students will choose a dataset from Kaggle about a topic that they care about.</p><p>They will then independently create a model to complete their goal (feel free to use the internet!)</p><p>Students will also create a presentation, i.e. google slides, explaining why they chose their topic and their methods used.</p>
Continue Final Projects
<p>Students will choose a dataset from Kaggle about a topic that they care about.</p><p>They will then independently create a model to complete their goal (feel free to use the internet!)</p><p>Students will also create a presentation, i.e. google slides, explaining why they chose their topic and their methods used.</p>
Final Project Presentations
<p>Share your final project to the class!</p>