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AI / ML
AI and ML for Data Engineers
From linear regression to transformers — every algorithm explained with analogies, code, and real-world use cases.
13 in-depth tutorials
AI and ML introduction: classification, regression, algorithms, and real-world use cases.
02Linear & Logistic Regression
The intuition-first guide to Linear and Logistic Regression. Linear Regression explained with the taxi meter analogy, the line equation, multiple features with weights, gradient descent as walking downhill blindfolded…
03Decision Trees & Random Forests
Master Decision Trees and Random Forests with the 20 Questions game analogy. How trees split using Gini Impurity, classification and regression trees, the overfitting problem with student memorization analogy, pruning…
Master XGBoost and Gradient Boosting with the iterative editor analogy. Bagging vs Boosting fundamental difference, how gradient boosting learns from residuals step-by-step, learning rate as volume knob on feedback.
The complete model evaluation guide. Why accuracy lies on imbalanced data (99% accuracy catching 0 frauds). Confusion matrix with fire alarm analogy.
The complete feature engineering guide. Why features matter more than algorithms (XGBoost with bad features loses to Logistic Regression with great features).
07Neural Networks & Deep Learning
How neural networks learn: neurons, activation functions, backpropagation, CNNs, RNNs, transformers, and embeddings, with worked examples and PyTorch code.
Text cleaning, stemming vs lemmatization, TF-IDF, text classification, the near-duplicate leakage trap, embeddings, topic modeling, and Spark ML.
Master LLM fine-tuning from concepts to code. Covers when to fine-tune vs RAG vs prompt engineering, LoRA and QLoRA methods, step-by-step with OpenAI API and Hugging Face, training data preparation, 5 real-world…
The complete guide to clustering algorithms for data engineers. K-Means explained step-by-step with Elbow Method and Python code, DBSCAN for density-based clustering with noise detection and eps tuning, Hierarchical…
Hyperparameter tuning: GridSearchCV, RandomizedSearch, Optuna, and cross-validation.
Baselines, Holt-Winters, SARIMA, and XGBoost compared on real results, plus the random-split leakage trap, walk-forward backtesting, and Spark at scale.
Taking models to production: MLflow tracking, Unity Catalog registry and aliases, batch scoring, drift monitoring with PSI, retraining, and CI/CD.