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5.0 /5
(13)

93
Bienvenidos al curso Fundamentos avanzados de ML
Anteriormente, en fundamentos de ML
Introducción
El repositorio GitHub del curso
Cómo sacarle el máximo partido al curso
Toma notas de tu curso en tiempo real en Frogames Formación
Estructuras de Datos y Algoritmos
Una breve historia de los datos
Una breve historia de los algoritmos
Aplicaciones de estructuras de datos y algoritmos en ML
Orden de Complejidad: Notación "Big O"
Ficheros de código y Google Colab
Orden de Complejidad: Tiempo Constante
Orden de Complejidad: Tiempo Lineal
Orden de Complejidad: Tiempo Polinómico
Tiempos de ejecución comunes y más sobre la notación 'Big O'
Ejercicios de Notación 'Big O'
Estructuras de Datos: Listas y Diccionarios
Notación Matemática: Postfix, Infix y Prefix
Listas
Arrays o Matrices
Listas Enlazadas y Doblemente Enlazadas
Stacks o Pilas
Queues o Colas y Deques
Implementación de pilas en Python
Practica las estructuras de datos en Python
Solución a la implementación de estructuras de datos en Python
Búsqueda y ordenación
Búsqueda Binaria
Algoritmo de la Burbuja
Merge Sort
Quick Sort
Resumen de Algoritmos de ordenación
Mapas o Diccionarios
Conjuntos
Hashing
Colisiones en el Hashing
Factor de Carga
Hash Maps y String Keys
La tabla ASCII
Ejercicios de hash maps
Hashings en Machine Learning
Ejercicios Finales de Listas y Diccionarios
Estructuras de Datos: Árboles y Grafos
Árboles
Árboles de decisión
Jack y Rose en el titanic (parte 2)
Ejercicios de árboles de decisión
Bosques aleatorios
Clasificación de flores con bosques aleatorios en Python
Ejercicios de Bosques Aleatorios
Árboles de Decisión Gradient-Boosted
XGBoost en acción
Ejemplos de Catboost
Ejercicios de Gradient Boosting
Otros conceptos de árboles
Grafos o Redes
Grafos dirigidos
Grafos acíclicos dirigidos (DAG)
Tensorboard
Más allá de los grafos
Donde todo confluye
Enfoque estadístico de la regresión
Machine/Deep Learning vs Estadística Frecuentista
El Descenso del Gradiente
Funciones objetivo o Criterios
El problema de medir el error en la predicción
Error Absoluto Medio
Error Cuadrático Medio
Minimización del coste con descenso de gradiente
Grafos acíclicos dirigidos en regresión lineal
Minimización del Costo con Descenso de Gradiente
Puntos críticos explicados
Puntos críticos en Python
Puntos críticos en espacios de dimensión superior
Mínimos globales vs locales
Descenso de gradiente estocástico (SGD)
Programación del ratio de aprendizaje
Tipos de programación del ratio de aprendizaje
Ascenso por gradiente
Optimización elegante en Machine Learning
Grafo Dirigido Acíclico de capa neuronal densa
Matrices jacobianas
Optimización de segundo orden
Matriz Hessiana
Momentos e impulso de Nesterov en ML
Optimizadores adaptativos
Optimizadores adaptativos (avanzado)
Trucos para elegir el optimizador adecuado
¿Qué es lo siguiente para ti?
Actividad Práctica: Optimización de un Modelo de Red Neuronal con Diferentes Optimizadores
Proyecto Final de Curso
Hemos terminado una aventura juntos, ¡pero vamos a por la siguiente!

instructor

5.0 /5
(13)

  • Avatar
    Jorge
    (5)
    That's exactly what I needed to get the full picture!

    I always saw those crazy formulas and got intimidated, but here everything is explained so you actually understand what's really going on. It's a challenge, yeah, but it's super satisfying to see how all the puzzle pieces fit together at the end. It gave me the confidence I needed to move forward in the world of data.

  • Avatar
    Silvana
    (5)
    Math That Actually Works

    These advanced fundamentals gave me the mathematical foundation I was missing to better understand Machine Learning models. Now I look at formulas with less fear and more curiosity. I feel like this course raised my technical level.

  • Avatar
    Giovanni
    (5)
    Solid foundation for the future

    This course finally helped me understand what's going on under the hood of algorithms. It's not just heavy theory; the instructor makes linear algebra and calculus click into practical sense, which gave me loads of confidence to move forward on more complex data projects without feeling like I'm just guessing

  • Avatar
    Javi
    (5)
    I finally got it

    I was afraid that math would get the better of me, but honestly it's explained in a super straightforward way. I feel like now I actually understand what's behind everything I do in programming without my head exploding. Highly recommended if you want to stop seeing this topic as a monster

  • Avatar
    Santiago
    (5)
    It becomes easy when it's hard. Excellent!

  • Avatar
    Nicolás
    (5)
    The foundation every data scientist needs!

  • Avatar
    Sara
    (5)
    The missing piece

  • Avatar
    Rosa
    (5)
    I loved how linear algebra, calculus, and probability connect with real algorithms

  • Avatar
    Nieves
    (5)
    Fundamentos Avanzados de Matemáticas para Machine Learning

    This course dives deep into the mathematical pillars that support modern Machine Learning. You'll learn linear algebra, differential and integral calculus, statistics, probability, information theory and optimization, all applied to real models. We work with Python, TensorFlow and PyTorch to visualize concepts like gradients, partial derivatives, matrices, eigenvectors and gradient descent.

  • Avatar
    Rocío
    (5)
    Excellent for understanding optimization and derivatives in neural networks.

  • Avatar
    danison
    (5)
    Solid Foundation to Move Forward

    This course is ideal for those who want to deepen their understanding of the mathematical fundamentals of Machine Learning. The explanations are detailed yet accessible, with examples that connect theory to practice.

  • Avatar
    Gabriela
    (5)
    The Foundation I Needed

    Essential for understanding the mathematical concepts behind Machine Learning. The explanations are in-depth but well-structured, with direct applications in trading algorithms and data analysis.

  • Avatar
    Samuel
    (5)
    It's really useful for any data scientist

    Now I feel like a real ML professional! I love how they guide you step by step

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