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This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ

This project is a collection of supervised and unsupervised machine learning algorithms implemented from scratch using Python. It serves as an educational resource for studying model training, parameter optimization, and the implementation of core predictive models. The library provides a variety of supervised learning tools, including linear and logistic regression, decision trees, and support vector machines. It also features unsupervised learning capabilities for discovering patterns in unlabeled datasets through clustering algorithms. Broad capability areas include ensemble learning thro

Dies ist ein umfassender Lehrplan, der darauf ausgelegt ist, die Grundlagen des Machine Learning mit der Programmiersprache Python zu vermitteln. Er bietet einen strukturierten Kurs, der die Implementierung und Theorie von überwachtem Lernen, unüberwachtem Lernen und Deep Learning abdeckt. Der Lehrplan wird durch interaktive Notebooks vermittelt, die ausführbaren Code mit technischen Tutorials kombinieren. Er enthält dedizierte Leitfäden zum Aufbau neuronaler Netzwerkarchitekturen, zur Implementierung von Klassifizierungs- und Regressionsmodellen sowie zur Nutzung von Clustering-Techniken zur Mustererkennung in ungelabelten Daten. Die Materialien decken den gesamten Machine-Learning-Workflow ab, einschließlich Datenvorverarbeitung und kategorialer Kodierung, Modelltraining und Hyperparameter-Tuning sowie Performance-Evaluierung. Er bietet zudem Tools zur Visualisierung des Modellverhaltens, wie z. B. das Plotten von Entscheidungsgrenzen und Entscheidungsbaumdiagrammen.

This repository is a collection of implementation references and solved notebooks covering supervised, unsupervised, and reinforcement learning techniques. It provides practical guides for building predictive models, clustering algorithms, and autonomous agents. The project includes specific implementations for neural network architectures, such as multi-layer perceptrons for digit recognition, and recommender systems using collaborative and content-based filtering. It also features reinforcement learning systems that utilize deep Q-learning to optimize decision-making policies. The codebase

Lit is a machine learning interpretability framework and model debugging tool designed to analyze model behavior and performance. It serves as an interpretability dashboard for large language models and a general performance analyzer for text, image, and tabular datasets. The project distinguishes itself through a comprehensive suite of interpretability tools, including salience map generation for feature attribution, the creation of synthetic and counterfactual examples to test robustness, and the projection of high-dimensional embeddings into visual spaces via UMAP or PCA. It further enable