Home Up PDF Prof. Dr. Ingo Claßen
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  • Visual Intro to Machine Learning (link)
  • Linear Algebra Concepts Every Data Scientist Should Know (link)
  • Table Transformer (TATR) (link)
  • Correlation vs. Regression: A Key Difference That Many Analysts Miss (link)
  • A New Coefficient of Correlation (link)
  • Frustration: One Year With R (link)
  • Precision & Recall (link)
  • An overview of time-aware cross-validation techniques (link)
  • Unsupervised Learning: What, Why, and Where? (link)
  • Does Isolation Forest really perform well in its task? (link)
  • EDA(Exploratory Data Analysis) On Haberman’s Cancer Survival Dataset (link)
  • Data Pre-processing in Python using Scikit-learn - Heart Disease Kaggle (link)
  • Matthews correlation coefficient - Tweet Raschka (link)
  • Supercharge Your Machine Learning Experiments with PyCaret and Gradio (link)
  • Feature Selection — Exhaustive Overview (link)
  • Introduction to Parallel Processing in Machine Learning using Dask (link)
  • Scikit-Learn: A silver bullet for basic machine learning (link)
  • Clustering using PyCaret!!! (link)
  • Data scientist’s guide to efficient coding in Python (link)
  • Applied Machine Learning: Part 1 (link)
  • How to avoid machine learning pitfalls: a guide for academic researchers (link)
  • A data science project - Analysis of Berlin rental prices (link)
  • The Normal Distribution Simplified (link)
  • Visualizing Statistics with Python — Telling Stories with Matplot (link)
  • Analyzing the eigenvalues of a covariance matrix to identify multicollinearity (link)
  • Gradient Descent for Machine Learning (link)
  • PM4PY - Process Mining in Python - Fraunhofer (link)
  • 26 Datasets For Your Data Science Projects (link)
  • Practical Machine Learning Tutorial: Part.1 (Exploratory Data Analysis) (link)
  • Data-Driven Artificial Intelligence (AI) for Churn Reduction (link)
  • Feature Transformation for Machine Learning, a Beginners Guide (link)
  • A Reference Notebook for 30+ Statistical Charts in Seaborn (link)
  • Multicollinearity — How does it create a problem? (link)
  • MAE, MSE, RMSE, Coefficient of Determination, Adjusted R Squared — Which Metric is Better? (link)
  • Essential Math for Data Science: Information Theory (link)
  • Bulldozer Prices Prediction (link)
  • 3 must-have projects for your data science portfolio (link)
  • Understand Bayes’ Theorem Through Visualization (link)
  • A Complete Exploratory Data Analysis with Python (link)
  • What’s in the Black Box? (link)
  • How to peek inside a black box model — Understand Partial Dependence Plot (link)
  • Pitfalls To Avoid while Interpreting Machine Learning-PDP/ICE case (link)
  • Understanding Probability Distribution (link)
  • Building 10 Regression Models in Machine Learning with Python (link)
  • First neural network for beginners explained (with code) (link)
  • Data Pre-Processing in Machine Learning with Python and Jupyter (link)
  • Building +10 Classifier Models in Machine Learning (link)
  • A field guide to the most popular parameters (link)
  • Customer Segmentation Analysis with Python (link)
  • Data Preparation and Data Binning (link)
  • Pipelines: Automated machine learning with HyperParameter Tuning! (link)
  • Correlation in Statistics (link)
  • Normal distribution (link)
  • Hierarchical Clustering: It’s just the order of clusters! (link)
  • Understanding AUC - ROC Curve (link)
  • Ridge Regression for Better Usage (link)
  • Data Pre-Processing in Machine Learning with Python+Notebook (link)
  • Entropy is a measure of uncertainty (link)
  • Support Vector Machine (link)
  • Multi-Dimensional Data (PCA) — boon or bane? (link)
  • Intuitions on L1 and L2 Regularisation (link)
  • Top Five Methods to Identify Outliers in Data (link)
  • Bengaluru House Price Prediction (link)
  • Bayes’ Rule Applied (link)
  • Starbucks offers: Advanced customer segmentation with Python (link)
  • How to Not Misunderstand Correlation (link)
  • Logistic Regression — Detailed Overview (link)
  • Introduction to Markov chains (link)
  • Scaling vs. Normalizing Data (link)
  • Chi-Square Test for Feature Selection in Machine learning (link)
  • Handling imbalanced datasets in machine learning (link)
  • Better Heatmaps and Correlation Matrix Plots in Python (link)
  • Logistic Regression Model Tuning with scikit-learn — Part 1 (link)
  • Building a Logistic Regression in Python (link)
  • Introduction to Bayesian Linear Regression (link)
  • Understanding Boxplots (link)
  • Patterns, Predictions, and Actions - Buch (link)
  • Gradient Descent in Python (link)
  • 17 types of similarity and dissimilarity measures used in data science (link)
  • Linear Regression using Gradient Descent (link)
  • Histograms and Density Plots in Python (link)
  • The Mathematics Behind Principal Component Analysis (link)
  • Probability concepts explained: Maximum likelihood estimation (link)
  • Fundamental Techniques of Feature Engineering for Machine Learning (link)
  • PCA using Python (scikit-learn) (link)
  • Machine Learning Basics with the K-Nearest Neighbors Algorithm (link)
  • Feature Selection with sklearn and Pandas (link)
  • How to Estimate the Bias and Variance with Python (link)
  • Comet - Supercharge Machine Learning (link)
  • Numerical Optimization: Understanding L-BFGS (link)
  • MLPerf (link)
  • Kaggle - Use Data from differnt Kernels (link)
  • Regular Expressions for Data Scientists (link)
  • Python Machine Learning (2nd Ed.) Code Repository (link)
  • Learning Math for Machine Learning (link)
  • Is R-squared Useless? (link)
  • Google Machine Learning Guides (link)

  • Machine Learning cheatsheets (link)

  • A Comprehensive Guide to Gradient Descent (link)
  • What’s the trade-off between Bias and Variance? (link)
  • Top 5 Machine Learning Algorithms Explained (link)
  • Encoding Categorical Variables in Machine Learning Dataset (link)
  • 17 Clustering Algorithms Used In Data Science and Mining (link)
  • Mathematics Ressources For ML (link)
  • LDA vs. PCA (link)
  • How to do matrix derivatives (link)
  • The Clustering Algorithm with Geolocation data (link)
  • The Poisson Distribution (link)
  • 9 Deadly Sins of Dataset Selection in ML (link)
  • Fraud detection — Unsupervised Anomaly Detection (link)
  • There is no classification — here’s why (link)
  • What Is Your Model Hiding? A Tutorial on Evaluating ML Models (link)
  • A Feature Selection Tool for Machine Learning in Python (link)
  • Transforming Scores Into Probability (link)
  • Probability vs Likelihood (link)
  • How to Remove Outliers for Machine Learning? (link)
  • Predicting House Prices in Ames, IA (link)
  • Using Random Forests to predict Housing Prices (link)
  • House Price Prediction using FastAI (link)
  • Customer Segmentation Using K Means Clustering (link)
  • Clustergam: visualisation of cluster analysis (link)
  • Bayes’ Theorem Unbound (link)

CRF

  • Performing Sequence Labelling using CRF in Python (link)
  • sklearn-crfsuite (link)
  • CRFsuite - Documentation (link)
  • Overview of Conditional Random Fields (link)
  • Conditional Random Fields for Sequence Prediction (link)
  • Getting started with Conditional Random Fields (link)
  • Introduction to Conditional Random Fields (link)

Curse of Dimensionality

  • What Is the Curse of Dimensionality? (link)
  • Curse of Dimensionality — A “Curse” to Machine Learning (link) Curse of Dimensionality - Notebook (link)
  • What is the Curse of Dimensionality? Simplest Explanation! (link)
  • Curse of Dimensionality (link)
  • Curse of Dimensionality - notebook (link)
  • The Curse of Dimensionality – Illustrated With Matplotlib (link)
  • The Curse of Dimensionality (part 1) (link)
  • Top 40 Curse of Dimensionality Interview Questions (link)

Embeddings

  • Vector Embeddings Explained for Developers! (link)
  • Explained: Tokens and Embeddings in LLMs (link)
  • Vector Embeddings 101: The New Building Blocks for Generative AI (link)
  • Meet AI’s multitool: Vector embeddings (link)
  • New and improved embedding model (link)
  • openai - embeddings (link)
  • Jurafski-Buch Kap 6 (link)
  • Jurafski-Buch Kap 6 - Folien (link)
  • A Guide on Word Embeddings in NLP (link)
  • The Beginner’s Guide to Text Embeddings (link)

explained.ai

Information Extraction

  • Twenty-five years of information extraction (link)

Metrics

  • Similarity Metrics in Vector Databases (link)
  • Distance Metrics in Vector Search (link)
  • 9 Distance Measures in Data Science (link)
  • Euclidean vs. Cosine Distance (link)
  • Cosine Similarity Vs Euclidean Distance (link)
  • When to use Cosine Similarity over Euclidean Similarity? (link)
  • Understanding Distance Metrics in Vector Embeddings: Cosine Similarity, Euclidean Distance, and Dot Product (link)
  • Understanding Vector Similarity for Machine Learning (link)
  • How the dot product measures similarity (link)
  • Similarity Measures: Check Your Understanding (link)

projects

  • Mapping Healthcare Access in Chicago (link)
  • Lessons from the Titanic Kaggle Dataset (Part 1): Aggresive Data Cleaning Isn’t Always Improve Model Accuracy (link)
  • Lessons from the Titanic Kaggle Dataset (Part 2): Which Features Matter Most in Predicting Survival? (link)
  • Diabetes Prediction Using Machine Learning Classification Approaches: A Capstone Project by Team Nabhan (link)