machinelearningmindset/machine-learning-course
Machine Learning Course
Dieses Projekt ist ein umfassender Lehrplan für das Erlernen von Data Science und prädiktiver Modellierung mit der Programmiersprache Python. Es bietet strukturiertes Lehrmaterial und Leitfäden zu überwachtem Lernen (Supervised Learning), unüberwachtem Lernen (Unsupervised Learning) und dem Design neuronaler Netze.
Der Lehrplan konzentriert sich auf das Erstellen, Trainieren und Evaluieren von Machine-Learning-Modellen. Er enthält spezifische Anleitungen zur Implementierung linearer Regression, Entscheidungsbäumen und Support Vector Machines für prädiktive Analysen sowie Tutorials zum Entwurf von Convolutional und Recurrent Neural Network Architekturen.
Der Kurs deckt ein breites Spektrum an Data-Science-Fähigkeiten ab, einschließlich der Evaluierung der Modellperformance durch Kreuzvalidierung, der Entdeckung verborgener Muster mittels Clustering und Hauptkomponentenanalyse sowie der Entwicklung von Deep-Learning-Modellen unter Verwendung geschichteter Berechnungsgraphen.
Das Lernen erfolgt über ein Notebook-basiertes interaktives Format, das ausführbaren Code mit beschreibendem Text kombiniert.
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.
