Links
Umfassende Sammlung nützlicher ML-Ressourcen: Videos, Tutorials, Bücher, Tools
Inhaltsverzeichnis
- 📺 StatQuest Videos (YouTube)
- Josh Starmer - Hauptkanal
- Confusion Matrix & Klassifikation
- Regression & Modellbewertung
- Gradient Descent & Optimierung
- Regularisierung
- Ensemble-Methoden
- Clustering & Dimensionsreduktion
- Cross Validation & Overfitting
- Neural Networks & Deep Learning
- 📝 Towards Data Science / Medium
- 🎯 Machine Learning Mastery
- 🎓 Google ML Crash Course
- 📄 Wissenschaftliche Paper
- 🎮 Interaktive Lerntools
- 📚 Bücher & E-Books (kostenlos)
- 🤖 KI-Tool-Sammlungen
- 🔗 Sonstige nützliche Ressourcen
- Tutorials & Artikel
- Tools & Frameworks
- GitHub Repositories
- Weitere
- 📺 Weitere YouTube-Kanäle
📺 StatQuest Videos (YouTube)
Josh Starmer - Hauptkanal
Confusion Matrix & Klassifikation
| Thema | Link |
| Confusion Matrix | Link |
| Sensitivity & Specificity | Link |
| ROC & AUC | Link |
Regression & Modellbewertung
| Thema | Link |
| R-squared | Link |
| Linear Regression | Link |
| Logistic Regression | Link |
Gradient Descent & Optimierung
| Thema | Link |
| The Chain Rule | Link |
| Gradient Descent | Link |
| Stochastic Gradient Descent | Link |
Regularisierung
| Thema | Link |
| Ridge Regression (L2) | Link |
| Lasso Regression (L1) | Link |
| Elastic Net | Link |
Ensemble-Methoden
| Thema | Link |
| Random Forest (Part 1) | Link |
| Random Forest (Part 2) | Link |
| XGBoost (Regression) | Link |
| XGBoost (Classification) | Link |
| XGBoost (Math Details) | Link |
| XGBoost (Optimizations) | Link |
Clustering & Dimensionsreduktion
Cross Validation & Overfitting
| Thema | Link |
| Cross Validation | Link |
| Overfitting | Link |
Neural Networks & Deep Learning
📝 Towards Data Science / Medium
| Thema | Link |
| Clean AutoML for Dirty Data | Link |
| Feature Scaling (MinMax/Standard) | Link |
| Multi-Class Confusion Matrix | Link |
| AUC-ROC Curve | Link |
| 9 Distance Measures | Link |
| XGBoost Deep Dive | Link |
| AWS AutoPilot (AutoML) | Link |
| CNN Guide (ELI5) | Link |
| LSTM Networks | Link |
| Shapley Values (XAI) | Link |
🎯 Machine Learning Mastery
| Ressource | Link |
| Homepage | Link |
| Imbalanced Classification Framework | Link |
| Master ML Algorithms (E-Book) | Link |
🎓 Google ML Crash Course
| Thema | Link |
| Embeddings | Link |
| Training and Loss | Link |
| Neural Network Nodes | Link |
📄 Wissenschaftliche Paper
| Paper | Link |
| Random Search for Hyperparameter Optimization (Bergstra & Bengio, 2012) | Link |
| Tool | Beschreibung | Link |
| Seeing Theory | Interaktive Statistik (Brown University) | Link |
| Teachable Machine | ML ohne Code (Google) | Link |
| CNN Explainer | Interaktiver CNN-Visualisierer | Link |
| Gradient Descent Visualizer | Interaktive Optimierung | Link |
| Neural Numbers | NN-Simulation | Link |
| Netflow (NN Simulation) | Forward/Backward Pass | Link |
| Reinforcement Learning Demo | RL interaktiv | Link |
| Sumory (RL Game) | RL-Spiel | Link |
| Polo Club (Georgia Tech) | ML-Visualisierungen | Link |
📚 Bücher & E-Books (kostenlos)
| Titel | Link |
| Data Science Guide (Berthold et al.) | Link |
| Hands-On ML (Géron) - Notebooks | Link |
| ML Book (Friedman) | Link |
| ML Interpretability (H2O) | Link |
| NLTK Book (NLP) | Link |
| Interpretable ML (Molnar) | Link |
| Data Mining Book | Link |
| Explanatory Model Analysis | Link |
| XAI Stories | Link |
| AI by Hand (Newsletter) | Link |
| Maschinennah (deutsch) | Link |
| Plattform | Link |
| There’s An AI For That | Link |
| Futurepedia | Link |
| That AI Collection | Link |
| Great AI Prompts | Link |
| Anthropic Economic Index: US Usage | Link |
🔗 Sonstige nützliche Ressourcen
Tutorials & Artikel
| Thema | Link |
| One-Hot Encoding (datagy) | Link |
| Target Encoding | Link |
| Logistische Regression (DATAtab, deutsch) | Link |
| PCA (Lamarr-Institut, deutsch) | Link |
| LDA (KnowledgeHut) | Link |
| Decision Tree Split | Link |
| Random Forests in MLlib (Databricks) | Link |
| Activation Functions | Link |
| Autoencoder Guide | Link |
| NLP (IBM) | Link |
| Shapley Value (Wallstreetmojo) | Link |
| Tool | Beschreibung | Link |
| Pandas | Datenmanipulation & -analyse | Dokumentation |
| NumPy | Numerische Berechnungen | Dokumentation |
| mlxtend | ML Extensions (Pattern Mining, Plotting) | Dokumentation |
| dtreeviz | Decision Tree Visualisierung | GitHub |
| SHAP | SHapley Additive exPlanations (XAI) | Dokumentation |
| LIME | Local Interpretable Model-agnostic Explanations | GitHub |
| ELI5 | Explain Like I’m 5 (Model Interpretation) | Dokumentation |
| InterpretML | Interpretable ML (Microsoft) | Dokumentation |
| Gradio | ML Demos & Interfaces | Link |
| PyCaret | AutoML Framework | Link |
| MediaPipe Studio | ML-Pipeline für Multimedia | Link |
| Plotly Chart Studio | Interaktive Visualisierungen | Link |
GitHub Repositories
| Repository | Link |
| ML From Scratch | Link |
| AWS ML University (NLP) | Link |
| DALEX Notebooks | Link |
| AI by Hand Exercises | Link |
| Hands-On ML Notebooks | Link |
Weitere
| Ressource | Link |
| KI-Campus | Link |
| IMAGINARY (Mathe-Visualisierung) | Link |
| Creative Commons BY 4.0 | Link |
| Python Package Quality | Link |
| Model Selection Guide | Link |
📺 Weitere YouTube-Kanäle
| Kanal | Link |
| The Morpheus Tutorials (deutsch) | Link |
| KNIME TV | Link |
Version: 1.0
Stand: Januar 2026
Kurs: Machine Learning. Verstehen. Anwenden. Gestalten.