Elite AI Mastery

The nested hierarchy of AI, ML, Deep Learning, and Generative AI.

Research Scientist vs. ML Engineer vs. AI Engineer — where you fit.

Market opportunity, salary ranges, and realistic timelines.

Vectors, matrices, dot products, and what a dimension means.

Derivatives, gradients, chain rule, and how gradient descent works.

Distributions, Bayes' Theorem, softmax, and the vocabulary of uncertainty.

Loss functions, gradient descent, learning rates, and Adam optimizer.

Variables, collections, logic, loops, functions, OOP, modules.

Arrays, vectorization, broadcasting, shaping — the language of ML data.

DataFrames, loading, cleaning, filtering, EDA.

Structured vs unstructured, public datasets, scraping, data licensing.

Missing values, outliers, normalization, encoding, train/test split, data leakage.

Creating features, scaling, selection, and why deep learning reduced but didn't eliminate it.

Summary stats, distributions, correlation, class imbalance detection.

Linear, multiple, polynomial regression, regularization, evaluation.

Logistic regression, decision trees, KNN, SVM, Naive Bayes.

Bagging, Random Forest, Boosting, XGBoost, LightGBM.

K-Means, hierarchical clustering, DBSCAN, anomaly detection.

Curse of dimensionality, PCA, t-SNE, UMAP.

Accuracy, precision, recall, F1, ROC, AUC, confusion matrix.

Diagnosing model failure, learning curves, regularization.

K-fold, grid search, random search, Bayesian optimization.

Weights, bias, activation functions, and the XOR problem.

Step-by-step how a neural network learns.

Vanishing gradients, batch norm, dropout, residual connections.

Loss curves, LR schedules, early stopping, mixed precision.

Tensors, autograd, nn.Module, training loop, DataLoader, GPU.

Choosing frameworks, ONNX, and the HuggingFace Hub.

Convolution, filters, pooling, feature maps, stride and padding.

ResNet, EfficientNet, pretrained weights, fine-tuning.

Object detection, segmentation, Vision Transformers, CLIP.

Tokenization, Bag-of-Words, TF-IDF, Word2Vec, GloVe, cosine similarity.

Sequence processing, vanishing gradient, gated memory.

Sentiment analysis, NER, translation, Seq2Seq, pre-Transformer attention.

Self-attention, QKV, multi-head attention, parallelization.

Positional encoding, encoder vs decoder, causal masking.

BERT, GPT, T5 — masked LM vs next-token prediction vs text-to-text.

BPE tokenization, pretraining, scaling laws, context windows, emergent abilities.

SFT, RLHF, reward models, DPO, and why alignment doesn't eliminate hallucination.

Why LLMs confabulate, knowledge cutoff, and mitigation strategies.

GPT, Claude, Gemini, Llama, Mistral, Qwen — choosing for cost, capability, privacy.

Zero-shot, few-shot, system prompts, chain-of-thought.

ReAct, self-consistency, structured output, prompt injection defense.

Embedding models, similarity metrics, vector databases, ANN search.

Chunking, hybrid search, re-ranking, evaluation — the most common LLM app pattern.

Prompt → RAG → fine-tuning decision order, data needs, cost.

LoRA, QLoRA, adapters — fine-tuning on consumer hardware.

Dataset building, evaluation, quantization, distillation.

Agent loop, tool calling, ReAct pattern, planning, memory.

Tool schemas, execution, guardrails, multi-tool orchestration.

Orchestrator patterns, failure modes, human-in-the-loop.

GANs, diffusion, latent space, text-to-image conditioning.

ASR, TTS, video generation, multimodal models.

Agent, environment, state, action, reward, exploration vs exploitation.

DQN, PPO, RLHF connection, and the future of reasoning models.

Model serving, Docker, batch vs real-time, model versioning.

Prompt versioning, token monitoring, caching, streaming, fallbacks.

Model drift, offline/online eval, LLM-as-judge, alerts.

CI/CD for ML, feature stores, experiment tracking, model registries.

Data bias, fairness metrics, auditing, explainability.

Prompt injection, privacy, guardrails, EU AI Act, model cards.

Which certs matter and in what order to pursue them.

Deployed projects, READMEs, open-source contributions.

ML theory, coding, system design — the three interview types.