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• 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

• home (link)
• The Mechanics of Machine Learning (link)
• rent.csv (link)

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)