machinelearningmindset/machine-learning-course
Machine Learning Course
Este proyecto es un plan de estudios educativo integral para aprender ciencia de datos y modelado predictivo utilizando el lenguaje de programación Python. Proporciona material de instrucción estructurado y guías que cubren el aprendizaje supervisado, el aprendizaje no supervisado y el diseño de redes neuronales.
El plan de estudios se centra en la construcción, el entrenamiento y la evaluación de modelos de aprendizaje automático. Incluye guías específicas para implementar regresión lineal, árboles de decisión y máquinas de vectores de soporte para análisis predictivo, así como tutoriales sobre el diseño de arquitecturas de redes neuronales convolucionales y recurrentes.
El curso cubre una amplia gama de capacidades de ciencia de datos, incluida la evaluación del rendimiento del modelo mediante validación cruzada, el descubrimiento de patrones ocultos mediante clustering y análisis de componentes principales, y el desarrollo de modelos de aprendizaje profundo utilizando grafos computacionales por capas.
El aprendizaje se imparte a través de un formato interactivo basado en notebooks que combina código ejecutable con texto descriptivo.
Features
- Predictive Model Development - Offers a comprehensive guide to the process of designing, training, and testing predictive machine learning models.
- Model Performance Evaluators - Provides tools and methods for quantifying predictive accuracy and establishing performance baselines.
- Neural Network Architectures - Provides instructional material on the design and function of convolutional and recurrent neural networks.
- Unsupervised Learning - Teaches how to discover hidden structures in unlabeled data using clustering and principal component analysis.
- Python Data Science Courses - Offers a comprehensive Python-based educational curriculum covering data science, predictive modeling, and ML frameworks.
- Interactive Notebook Learning Resources - Delivers educational content through interactive Jupyter notebooks combining executable code cells with descriptive text.
- Predictive Modeling Curricula - Provides a structured learning path for building, training, and evaluating machine learning models.
- Linear Regression - Covers the fundamental use of linear regression to establish performance baselines for predictive modeling.
- Machine Learning Implementations - Provides code-based implementations of decision trees and support vector machines for mapping input data to targets.
- Hybrid Convolutional Recurrent Networks - Covers the design of hybrid networks combining convolutional layers for feature extraction and recurrent layers for temporal dependencies.
- Neural Networks - Teaches the design of multi-layered computational architectures including convolutional and recurrent networks.
- K-Fold Cross-Validation - Implements K-fold cross-validation pipelines to ensure model generalisation and prevent overfitting.
- TensorFlow Model Definitions - Teaches how to build deep learning models using TensorFlow's layered computational graphs.
- Dimensionality Reduction - Implements principal component analysis to reduce high-dimensional data into lower-dimensional representations.
- Unsupervised Learning - Provides educational content on identifying patterns in unlabeled data through clustering and PCA.
- Neural Network Tutorials - Provides tutorials on implementing convolutional and recurrent architectures for processing complex data patterns.
- Machine Learning Guides - Provides guides for applying Python to implement linear regression, decision trees, and support vector machines.
- Scikit-Learn Examples - Provides practical code demonstrations using Scikit-Learn for implementing decision trees and support vector machines.
- Learning and Reference - ML course with Python.
- Machine Learning Algorithms - Structured course material covering essential machine learning concepts.
- Interview Preparation - A structured course for learning machine learning concepts.
