Aprendizaje automático con Tensorflow | Python nivel 5
Este curso de aprendizaje automático enseña conceptos esenciales como TensorFlow, regresión lineal, redes neuronales y ajuste de modelos a través de proyectos prácticos. Los estudiantes aplican sus habilidades utilizando conjuntos de datos y crean sus propios proyectos finales de aprendizaje automático.
AI Code Academy
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4.7
Número de reseñas:(1,743)
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Qué está incluido
10 reuniones en vivo
10 horas presencialesProyectos
2-4 horas por semana. Projects are not mandatory but we strongly encourage students to complete them.Evaluación
incluidoExperiencia de clase
This comprehensive Machine Learning Fundamentals course covers essential concepts and practical applications across ten structured lessons. Beginning with an introduction to TensorFlow and basic machine learning principles, students progress through topics such as linear regression, model tuning strategies, K-NN clustering, decision forests, and neural networks, including feed-forward networks and convolutional neural networks (CNNs). Each lesson incorporates hands-on projects using datasets like MNIST and CIFAR10, where students implement algorithms, tune models for optimal performance, and visualize results. The course culminates in a final project phase where participants develop and present their own machine learning applications, exploring diverse topics from image classification to model ensembling and hyperparameter optimization. For a week to week program, check out the syllabus.
Metas de aprendizaje
This Machine Learning Fundamentals course covers TensorFlow introduction, basic principles, linear regression, model tuning, K-NN clustering, decision forests, and neural networks like CNNs.Through hands-on projects with MNIST and CIFAR10 datasets, students implement algorithms, optimize models, and present final projects on topics including image classification and hyperparameter optimization.
Programa de estudios
10 Lecciones
más de 10 semanasLección 1:
Intro To TensorFlow
Objectives:
Learn the basics of machine learning
What is learning?
History of machine learning
Regression vs classification
Introduce TensorFlow
Installing TensorFlow
What is a Tensor?
Low-level API and high-level API
How do Tensors perform computations?
Tensorflow variables
Project 1: Perform basic computations with TensorFlow
Matrix addition, matrix multiplication using tensors in TensorFlow 60 minutos de lección en vivo en línea
Lección 2:
Linear Regression
This project aims to teach linear regression fundamentals, including hypothesis setup with the equation 𝑦 = 𝑚 𝑥 + 𝑏 y=mx+b, where 𝑚 m represents weights and 𝑏 b denotes biases. Participants will learn to compute these parameters using gradient descent and understand the mean squared error cost function. Using TensorFlow and NumPy, they will implement a linear regression model, iteratively refining weights and biases over multiple epochs and visualizing model performance. 60 minutos de lección en vivo en línea
Lección 3:
Model Tuning
Objectives
Why do we need to tune models?
Convergence problems
Preparation
Strategies for tuning models
Grid Search
Random Search
Data transformation
Project 3: Tune the linear regression model to produce better results
Implement Grid Search
Implement Random Search
Implement a data transformation 60 minutos de lección en vivo en línea
Lección 4:
K-NN Clustering
Objectives:
How do we solve classification problems?
Problems with classification
Strategies and models to use
K-NN Algorithms
How do clustering algorithms work?
How does the K-NN algorithm perform classification
How do we measure distance?
What is the effect of the K hyperparameter?
Project 4: MNIST Classification with K-NN
Import the dataset and process it
Implement the K-NN algorithm
Visualize the results
Tune the model for better results 60 minutos de lección en vivo en línea
Otros detalles
Recursos externos
Los estudiantes no necesitarán utilizar ninguna aplicación o sitio web más allá de las herramientas estándar de Outschool.
Se unió el April, 2020
4.7
1743reseñas
Popular
Experiencia y certificaciones del docente
**USE PROMO CODE: CODEAIPROMO10 FOR $10 OFF ANY COURSE - Valid until Nov, 25 **
~We offer early registration, sibling discounts, and multi-course bundles. ~
~Check out our complete Outschool offering here: https://shorturl.at/bcBGP ~
At AI Code Academy, we specialize in project-based STEM coding, AI, and mathematics programs for young learners. We are one of the few organizations that offer AI and machine learning courses tailored for kids. Our comprehensive curriculum spans from basic computer skills and Scratch coding to more advanced Python, Java, web design, game development, and AI machine learning projects.
Our unique focus is on introducing students to AI early, helping them grasp complex concepts like machine learning, data analysis, and smart devices, while also reinforcing mathematics skills, essential for their success in STEM fields.
With a team of passionate instructors—college students and recent graduates with degrees in Engineering and Computer Science—we provide hands-on, real-world projects that prepare students for future careers in AI, coding, robotics, and mathematics.Reseñas
Curso grupal en vivo
27 US$
semanalmente o 269 US$ por 10 clases1 x por semana, 10 semanas
60 min
Completado por 8 alumnos
Videoconferencias en vivo
Edades: 13-18
4-8 alumnos por clase
Asistencia financiera
Obtener la aplicación
Tutoría
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