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Campamento de verano: Aprendizaje automático en Python con Big Data (Nivel 4)
En este curso en vivo en línea de 10 lecciones, los estudiantes aprenderán a entrenar proyectos de aprendizaje automático de IA con Python utilizando el reconocimiento de imágenes y la predicción de números, como clasificación de flores, predicción del precio de las acciones y predicción del draft de la NBA.
Experiencia de clase
This course is designed to provide students with a comprehensive introduction to machine learning concepts and practical applications. It begins with foundational lessons on setting up tools like AICode101, followed by projects that progressively build skills in image recognition, weather prediction, and stock market analysis. Students will learn to manipulate data, generate models, and analyze results using platforms like AICode101 and Google Colaboratory. The course culminates in a review...
10 lessons//2 Weeks
Week 1Lesson 1OverviewDesigned to provide students with a very basic understanding of machine learning concepts that they will be able to apply in the course. They will also be shown the tools they will be using throughout the course. Task: Set up AICode101 Accounts Students will be tasked with creating AICode101 accounts and walking through the website to learn how it will be used in the course.Lesson 2Image Recognition Project with AICode101Designed to provide students with an introduction to image recognition in machine learning. Students will be able to apply concepts in machine learning to generate their own program to recognize categorized images. Task: Image Recognition Project Students will be tasked with creating an AICode101 image recognition project. Through this, they will learn key fundamentals of image recognition in machine learning.Lesson 3Rain in Australia’ Number Project with AICode101Designed to provide students with an introduction to classification with data in machine learning. Students will be able to apply concepts in machine learning to generate their own program to predict weather. Task: Number Project Students will be tasked with creating an AICode101 number project. Through this, they will learn key fundamentals of classification in machine learning.Lesson 4The Stock Market FundamentalsDesigned to provide students with an introduction to the stock market in preparation for the next lesson. Students will learn about what the stock market is and how it can be incorporated into machine learning. Task: Stock Market Search Students will be tasked with navigating financial sites to view different stocks and the associated data that defines a stock and its performance.Lesson 5Stock Price Prediction with AICode101Designed to provide students with an introduction to data manipulation for use in machine learning and functional data classification through the use of models. Task: Data Analysis and Cleanup Students will be given data to analyze for important features and “clean up” the data to ensure no issues in calculation or model generation. Task: Model Generation Students will generate models using the data they have handled to make predictions.Week 2Lesson 6Flower Classification Project with Google ColaboratoryThis course introduces students to machine learning using Python, focusing on data handling, model creation, and prediction using Google Colaboratory. Tasks include data analysis and cleanup, where students manipulate and visualize data, and model generation to classify flower species, followed by evaluating model performance using various techniques.Lesson 7Stock Market Price Prediction with Google ColaboratoryThis advanced course builds on fundamental machine learning concepts, enabling students to apply their knowledge to deeper insights and practical applications. Tasks include loading and analyzing data for cleanup and visualization in notebooks, and generating models to predict stock prices based on historical data, with subsequent analysis to assess model effectiveness.Lesson 8NBA Draft Pick Prediction with Google ColaboratoryThis advanced course deepens students' understanding of machine learning fundamentals, focusing on practical applications. Tasks include loading, cleaning, visualizing data in notebooks, and generating models to predict NBA draft positions based on player statistics, followed by evaluating model performance using various analytical methods.Lesson 9Class Review with KahootStudents will review all of the material learned in the course in a fun review class. The Kahoot quiz software will be used to provide a fun, interactive experience for students.Lesson 10Final Project PresentationsStudents will present their final projects based off of data provided to them in a previous class. They will have time to work on their projects at the beginning of class.
- This course introduces students to machine learning fundamentals through practical projects using AICode101 and Google Colaboratory
- They will develop skills in image recognition, weather prediction, stock market analysis, and culminate with a final project presentation showcasing their application of machine learning concepts.
2 - 4 horas semanales fuera de clase
Proyectos
Frecuencia: 1-2 durante toda la claseComentario: incluidoDetalles: Projects are not mandatory but we strongly encourage students to complete them.Evaluación
Frecuencia: incluidoDetalles:
Reseñas
Clase grupal
279 US$
por 10 clases5 x por semana, 2 semanas
75 min
Completado por 4 alumnos
Videoconferencias en vivo
Edades: 13-18
4-10 alumnos por clase