No Hype AI: Beyond GenAI

A map of the full AI/ML landscape in 2026 — where generative AI fits and the major branches beyond it: classical ML, deep learning, computer vision, NLP, reinforcement learning, and the non-generative systems that quietly run most of industry.

Core ML literacy — supervised, unsupervised, and reinforcement learning, the train/evaluate/iterate workflow, and concepts like overfitting, cross-validation, and the bias-variance trade-off, hands-on with scikit-learn.

The computer-vision stack behind manufacturing, autonomy, and document processing — object detection (YOLO), segmentation (SAM, Mask R-CNN), pose estimation, OCR, transfer learning, and edge deployment.

The NLP that is not a chatbot — named entity recognition, sentiment, classification, topic modelling, and document understanding, and when focused encoder models (BERT family, spaCy) beat an LLM on speed, cost, and consistency.

AI built for temporal data — anomaly detection, demand forecasting, and predictive maintenance with ARIMA, exponential smoothing, Prophet, and deep models, and why LLMs are poor at numerical forecasting.

How recommendation engines work — collaborative and content-based filtering, hybrid recommenders, the cold-start problem, and the metrics (precision@k, NDCG, coverage) unique to recommendations.

From notebook to reliable production — experiment tracking (MLflow, Weights & Biases), model versioning, serving (batch vs real-time), drift monitoring, and automated retraining across the MLOps lifecycle.

A rigorous decision framework for choosing classical ML, deep learning, or generative AI for a problem — by latency, cost, interpretability, data availability, accuracy, and maintenance burden, worked through real case studies.

Hybrid architectures where classical ML and LLMs work together — routing with a fast classifier, ML pre-filtering before LLM calls, LLM feature extraction into classical models, and LLM-generated training data.

The AI career landscape in 2026 — ML engineer, data scientist, computer-vision and NLP specialist, MLOps engineer, research scientist, and emerging hybrid roles, and how to build a durable personal learning path.