Outschool
Abrir configuración de moneda, zona horaria e idioma
Iniciar sesión

Inteligencia artificial (IA) individual para estudiantes de secundaria

Preséntale a tu hijo adolescente la inteligencia artificial (IA) y el aprendizaje automático (AA) en esta clase práctica de conceptos y codificación. Dominarán Python, explorarán herramientas como ChatGPT y usarán herramientas y habilidades de la industria que se utilizan en la universidad y en carreras tecnológicas.
Clase

Qué está incluido

8 reuniones en vivo
6 horas 40 minutos horas presenciales
Tarea
1 hora por semana. If requested by the student or parent, required reading from informational AI blogs, articles, or tutorials can be assigned.
Tareas
If requested by the student or parent, extra coding activities related to the class can be assigned for extra practice.
Proyectos
The course will end in a final project where the learner will program, train, and evaluate a machine-learning model. This project will challenge the learner, so much of the work will be done in class where the instructor can assist with it. If needed, some time may be required outside of class to work on it.
Certificado de finalización
A certificate of completion will be granted to highlight that the student has demonstrated a strong understanding of the fundamentals of artificial intelligence and machine learning.

Experiencia de clase

Nivel de inglés: desconocido
Grado de EE. UU. 9 - 12
Nivel Beginner
What will be taught?

Please see the syllabus for a more detailed guideline of an example structure for the class. This is a personalized 1-on-1 course and the content will be adapted for individual learners. 

Week 1: Introduction to AI and ML. Learners will understand key concepts of AI and ML, including their differences, types, and applications. They'll also explore ChatGPT and prompt engineering, learning how to interact with generative AI tools.

Week 2: Python Foundations and ML Concepts. The course will cover Python basics, essential for AI programming, and introduce key ML algorithms such as Linear Regression, K-Nearest Neighbors, and Decision Trees.

Week 3: Hands-On Coding with ML Libraries. Students will explore data analysis and visualization techniques, and use real-world libraries that are used by professional software and ML engineers to build and evaluate ML models.

Week 4: Deep Learning and Neural Networks. The final week focuses on neural networks using more real-world libraries, culminating in a project where learners build their own neural network.



How is the class structured?

The class is designed for one-on-one interaction, ensuring personalized attention and support. Learners will have opportunities to engage with the instructor through Q&A sessions, and interactive exercises will help solidify their understanding. Each week consists of two lessons, delivered via live Zoom sessions. 

Each lesson will include: 
-A brief review quiz
-A unique hands-on activity or coding exercise
-An interactive personalized 1-on-1 lecture or demonstration
-Slides that support the lectures
-Plenty of space for curiosity, questions, and student exploration



What is my teaching style?

I believe that the practice of having a large group of students sit down and passively absorb information from the teacher is outdated and ineffective. My teaching philosophy revolves around personalized active engagement. In this class, students will have plenty of opportunity to explore their unique interests and spend more time on what they're passionate about. A clear and practical syllabus and accompanying content is also provided for learners who prefer a more guided and structured course. The class is hands-on and fun, making sure that students enjoy learning and remember the concepts better. 



Who is this class for?

Any high-school aged learner can attend! No background or prerequisites are necessary, but it is highly recommended to have experience in a programming language. Python is preferred, because that is the language used in this course. This is a personalized course and we will adapt to any learner experience. For learners without coding experience, we can either cover more high-level concepts or spend more time on coding basics. There are also many great class options for learning Python or other programming languages on Outschool!
Metas de aprendizaje
Students can identify differences between AI and traditional coding.
Students have a basic understanding of at least one ML model and can explain what it does.
objetivo de aprendizaje

Programa de estudios

4 Unidades
8 Lecciones
más de 4 semanas
Unidad 1: Introduction to Artificial Intelligence (AI) and Machine Learning (ML)
Lección 1:
Understanding AI and ML
 • Get to know each other
• What to expect from this class
• What is AI
• AI vs traditional coding
• Types of AI (narrow vs general)
• Types of AI problems: Computer Vision (images), Natural Language Processing (language), Time Series (stock market and weather)
• AI vs ML
• What is ML
• Types of ML: Supervised Learning, Unsupervised Learning, and Reinforcement learning
• Activity: Peanut Butter Jelly 
50 minutos de lección en vivo en línea
Lección 2:
ChatGPT, Generative AI, and Prompt Engineering
 • ChatGPT
• Applications of ChatGPT
• Generative AI
• Hallucinations
• Prompt Engineering: Chain of Thought Prompting, Few-Shot Learning, Role Playing
• Activity: ChatGPT Playground 
50 minutos de lección en vivo en línea
Unidad 2: Python Foundations and ML Concepts
Lección 3:
Python Review and Basics
 • Python basics
• Data types, variables, loops, functions
• Google Colab
• Activity: Python Coding Exercise 
50 minutos de lección en vivo en línea
Lección 4:
Machine Learning Algorithms
 • Linear Regression
• K-Nearest Neighbors
• Decision Trees
• Data labels
• Activity: Building Decision Tree 
50 minutos de lección en vivo en línea

Otros detalles

Orientación para padres
This course includes the use of third-party tools such as Google Colab and ChatGPT. A google account is necessary for Google Colab. No account is necessary for ChatGPT because all prompts will be submitted and screen shared through the instructor.
Requisitos previos
This class is individually tailored to your student's current ability. No prerequisites are necessary. In order to get the full experience from the class, we recommend knowledge in at least 1 programming language (Python preferred).
Idioma en el que se imparte la clase
Inglés
Se unió el May, 2024
Perfil
Experiencia y certificaciones del docente
Licenciatura en Ciencias de la Computación desde North Dakota State University
I hold a Master’s degree in Computer Science from North Dakota State University, where I specialized in Artificial Intelligence (AI) and Machine Learning (ML). My academic background is further strengthened by three certifications from the NVIDIA Deep Learning Institute, covering advanced AI and ML topics.

My professional experience includes roles as an AI researcher and as an AI and ML focused software engineer. In these roles, I applied AI and ML techniques to real-world problems, such as classifying agricultural crops and determining the edibility of mushrooms. This hands-on industry experience allows me to teach AI concepts with a practical, real-world focus.

In addition to my technical expertise, I have a strong foundation in teaching complex computer science topics. I served as a Learning Assistant at North Dakota State University, where I supported students in mastering data structures and algorithms—a critical and challenging area of study in computer science. 

Reseñas

Curso privado en vivo
Compartir

176 US$

semanalmente o 702 US$ por 8 clases
2 x por semana, 4 semanas
50 min

Videoconferencias en vivo
Edades: 14-18

Acerca de
Apoyo
SeguridadPrivacidadPrivacidad de CAPrivacidad del alumnoAdministrar preferencias de datosTérminos
Obtener la aplicación
Descargar en la App StoreDescargar en Google Play
© 2024 Outschool