Attention, Transformers, and Large Language Models (LLMs) Explained (Chapter 8.3) AI FUNDAMENTALS by aimlrevisited aimlrevisited - October 22, 20260 Master Transformers and LLMs: understand Self-Attention (Q, K, V), Multi-Head Attention, positional encoding, and generative pre-training.
Responsible AI: Ethics, Algorithmic Bias, Explainability (XAI), and Safety Explained (Chapter 8.4) AI FUNDAMENTALS by aimlrevisited aimlrevisited - October 22, 20260 Master Responsible AI: audit algorithmic bias, explain black-box models using SHAP and LIME, and implement AI safety guardrails.
Data Cleaning for Machine Learning: Handling Missing Values, Outliers, and Noise (Chapter 3.2) AI FUNDAMENTALS by aimlrevisited aimlrevisited - October 22, 20260 Master data cleaning for AI: handle missing values with KNN and median imputation, detect outliers via IQR and Z-scores, and build robust Python pipelines.
Linear & Polynomial Regression: Predicting Continuous Values (Chapter 4.2) AI FUNDAMENTALS by aimlrevisited aimlrevisited - October 22, 20260 Master linear and polynomial regression for AI: explore Ordinary Least Squares, gradient descent fitting, MSE/RMSE cost metrics, and Python code.
Logistic Regression: From Linear Scores to Classification Probabilities (Chapter 4.3) AI FUNDAMENTALS by aimlrevisited aimlrevisited - October 22, 20260 Master logistic regression for AI: explore the Sigmoid function, log-odds, binary cross-entropy loss, decision thresholds, and Softmax classification.
Distance & Probabilistic Classifiers: K-Nearest Neighbors (KNN) and Naive Bayes (Chapter 4.4) AI FUNDAMENTALS by aimlrevisited aimlrevisited - October 22, 20260 Master KNN and Naive Bayes for AI: compare lazy vs eager learning, distance metrics, Bayes theorem with conditional independence, and Scikit-Learn code.
Support Vector Machines (SVM) and Decision Trees: Maximum Margins and Recursive Splitting (Chapter 4.5) AI FUNDAMENTALS by aimlrevisited aimlrevisited - October 22, 20260 Master SVMs and Decision Trees for AI: explore maximum margin hyperplanes, the RBF kernel trick, Gini impurity, tree pruning, and Scikit-Learn code.
Ensemble Learning: Bagging, Random Forests, and Gradient Boosting Explained (Chapter 4.6) AI FUNDAMENTALS by aimlrevisited aimlrevisited - October 22, 20260 Master ensemble learning for AI: explore bootstrap aggregating (Bagging), Random Forests, gradient boosting with XGBoost and LightGBM, and Python workflows.
Train, Validation, and Test Splits & Cross-Validation Strategies Explained (Chapter 5.1) AI FUNDAMENTALS by aimlrevisited aimlrevisited - October 22, 20260 Master train-test splits and cross-validation for AI: explore hold-out validation, Stratified K-Fold, Time-Series splits, and Scikit-Learn workflows.
Mastering Machine Learning Evaluation Metrics: Precision, Recall, and ROC-AUC Explained (Chapter 5.2) AI FUNDAMENTALS by aimlrevisited aimlrevisited - October 22, 20260 Master ML evaluation metrics: understand the Confusion Matrix, the precision-recall trade-off, ROC-AUC, F1-score, and regression metrics with Python code.