🧠 Machine Learning Fundamentals: Sub-Curriculum Index

Welcome to the Machine Learning Fundamentals curriculum. This track provides software engineers with an intuitive, mathematical, and algorithmic foundation in statistical learning: ML paradigms, feature engineering pipelines, supervised vs. unsupervised algorithms, Bayesian probabilistic inference, and robust model evaluation metrics.

Every guide in this series strictly follows a two-part learning format:

  • ⚡ Quick Dive: Algorithm comparison matrices, metric formulas, and scikit-learn code snippets.
  • 📖 Extended Guide: Deep mathematical formulations, loss function derivations, validation strategies, and production pitfalls.

📚 Curriculum Roadmap

# Guide Primary Topics Covered
01 AI Concepts for Software Engineers High-level taxonomy of AI, ML, Deep Learning, and generative models from a software engineering perspective.
02 Machine Learning Paradigms Supervised, unsupervised, reinforcement learning, inductive bias, and the bias-variance tradeoff.
03 Feature Engineering & Data Pipelines Numerical scaling (StandardScaler, MinMax), categorical encodings (One-Hot, Target), feature selection, and imputation.
04 Supervised & Unsupervised Learning Linear/Logistic Regression, Decision Trees, Random Forests, Gradient Boosted Trees (XGBoost/LightGBM), K-Means, and PCA.
05 Bayesian Learning & Probabilistic Models Bayes theorem, Maximum Likelihood Estimation (MLE), Maximum A Posteriori (MAP), Naive Bayes, and uncertainty estimation.
06 Model Evaluation Metrics & Validation Confusion matrix, Precision/Recall, F1-score, ROC-AUC, Log-Loss, k-fold cross-validation, and data leakage prevention.
⌂ All Guides