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Master Theory & Concepts with Adaptive AI.
Master Theory & Concepts across 65 patterns - Theory & Concepts - guided by Leyaa's adaptive intelligence.
6Topics
65Patterns
15hEst. duration
Theory & Concepts Course
6 topics
Evaluation
8 patterns
Deep Learning
17 patterns
ML Foundations
10 patterns
Production ML
9 patterns
Data & Features
10 patterns
Classical ML
11 patterns
Curriculum
Everything You'll Learn
Explore every pattern in the course.
Theory & Concepts
6 topics · 65 patterns01
Evaluation
8 patterns
A/B Testing for ML Models - Holdout, Interleaving, Causal InferenceCalibration - Why Accuracy Isn't Enough for Probability OutputsClassification Metrics - Precision, Recall, F1, When Each MattersOffline vs Online Evaluation - Why They DisagreeRanking Metrics - NDCG, MAP, MRR - How Search and Rec Systems Measure QualityRegression Metrics - MAE, MSE, RMSE, MAPE, Huber - When to Use WhichROC-AUC vs PR-AUC - Imbalanced Classes, Why AUC MisleadsStatistical Significance in ML - P-Values, Confidence Intervals, Effect Size
02
Deep Learning
17 patterns
Activation Functions - ReLU, Sigmoid, GELU, Why ReLU DominatesAttention Mechanism - Why It Replaced RecurrenceBackpropagation - Chain Rule, Gradient Flow, Vanishing GradientsBatch Normalization vs Layer Normalization - Why and WhenComputer Vision - Object Detection, YOLO vs R-CNN TradeoffsConvolutional Neural Networks - Receptive Field, Pooling, Inductive BiasDropout Regularization - Intuition, Training vs InferenceEmbeddings - Word2Vec to Contextual, How Meaning Becomes VectorsGraph Neural Networks - Message Passing, When Data Is a GraphImage Segmentation - Semantic vs Instance vs PanopticLSTM and GRU - The Gating Solution to Long-Range DependenciesOptimizers - SGD, Adam, AdaGrad, Learning Rate SchedulesRecurrent Neural Networks - Vanishing Gradient, Gating MechanismsTokenization - BPE, WordPiece, SentencePiece, Why It Matters for LLMsTransfer Learning and Fine-Tuning - Why Pretrain Then FinetuneTransformer Architecture - Self-Attention, Positional Encoding, Scaling LawsWhy Deep Learning - What Shallow Networks Cannot Represent
03
ML Foundations
10 patterns
Bias-Variance Tradeoff - The Fundamental Tension in MLCross-Validation - K-Fold, Stratified, Time Series SplitsData Splits - Train, Validation, Test - Why Three SetsHow to Frame a Business Problem as an ML ProblemOverfitting and Underfitting - Diagnosing and FixingSupervised vs Unsupervised vs Reinforcement Learning - When to Use WhichThe Curse of Dimensionality - Why High Dimensions HurtThe ML Development Lifecycle - From Problem to ProductionThe No Free Lunch Theorem and What It Means for Model SelectionWhy Machine Learning Exists and What Rules Cannot Solve
04
Production ML
9 patterns
Continual Learning vs Periodic Retraining - TradeoffsError Analysis - Slice-Based Evaluation, Finding Failure ModesExperiment Tracking - MLflow, W&B, Model Registry, LineageMLOps - CI/CD for ML, Automated Training, Testing, Deployment PipelinesModel Debugging - Systematic Diagnosis of ML FailuresModel Interpretability - SHAP, LIME, Attention, When Black Box FailsMonitoring ML Models in Production - Data Drift, Performance DecayPrompt Engineering - Chain-of-Thought, Few-Shot, RAG Prompting PatternsResponsible AI - Fairness, Bias Detection, Mitigation Strategies
05
Data & Features
10 patterns
Data Labeling Strategies - Human Annotation, Weak Supervision, ProgrammaticData Leakage - The Silent Killer of ML ModelsDealing with Distributional Shift - Covariate, Concept, Label DriftFeature Engineering - Why Features Matter More Than ModelsFeature Scaling - Normalization vs Standardization, When Each WorksFeature Selection - Filter, Wrapper, Embedded MethodsHandling Class Imbalance - Oversampling, Undersampling, Focal LossHandling Missing Data - Imputation Strategies and When to DropOnline vs Offline Data Collection - Tradeoffs at ScaleSampling Strategies - Random, Stratified, Reservoir, Weighted
06
Classical ML
11 patterns
Decision Trees - Information Gain, Gini Impurity, PruningDimensionality Reduction - PCA, t-SNE, UMAP, When to Use WhichGradient Boosting - XGBoost, LightGBM, Why Boosting Beats BaggingK-Means Clustering - Initialization, Convergence, Failure ModesK-Nearest Neighbors - Curse of Dimensionality, Approximate NNLinear Regression - From Least Squares to Gradient DescentLogistic Regression - Decision Boundary, Log-Odds, CalibrationNaive Bayes - Conditional Independence, When It Still WinsRandom Forests - Bagging, Feature Randomness, OOD RobustnessRegularization - L1 Lasso, L2 Ridge, Elastic Net, Why Each WorksSupport Vector Machines - Margin, Kernel Trick, When to Use
Why LeyaaNot just a course. A learning intelligence.
Leyaa doesn't just deliver content - it understands where you are, what you need, and how to get you there fastest.
Adaptive Diagnostics
Leyaa identifies which of the 65 patterns you've mastered and which need work - before you waste time on what you already know.
Pattern-Level Precision
Every pattern gets a verdict - Learn, Practice, or Skip - so your study time goes exactly where it counts.
Intelligent Pacing
Leyaa reads your velocity across the whole course and adapts your focus list dynamically, keeping you in the optimal learning zone.
Before & After Visibility
Every assessment creates a snapshot. Retake anytime to see exactly how much you've grown - pattern by pattern.
Learner Autonomy
Leyaa recommends, never restricts. Override any suggestion and explore at your own pace - the system tracks everything regardless.
Clear Learning Path
A structured journey through 6 topics, where each one sets you up perfectly for the next.
Theory & Concepts
Ready to master Theory & Concepts?
Start your journey today
Start your diagnostic now. Build mastery across all 65 patterns in under 15 minutes.
65 Patterns
Personalized roadmap
