Transformers Documentation

Category

LLM Engineering

Provider

Hugging Face

Type

course

Level

build

Topic

LLM Engineering

Track

LLM Engineering

Section

n/a

Format

n/a

Status

publishable

Commercial

unknown

Featured

no

Fast start

no

Sequence

n/a

Priority

n/a

Primary source

learning_paths

Sources

learning_paths

ID

801f6f032deb29a3

scikit-learn documentation

scikit-learn

Still the fastest way to build strong baselines and classical ML systems.

XGBoost Documentation

XGBoost

Strong tabular baseline models; learn training and evaluation patterns.

PyWavelets Documentation

PyWavelets

Wavelet transforms for non-stationary vibration analysis and fault signatures.

PyPSA Documentation

PyPSA

Open-source power system analysis; build grid optimization + planning pipelines.

pandapower Documentation

pandapower

Power flow and grid modeling with Python; bridges data → simulation → control.

OpenModelica Documentation

OpenModelica

Model-based engineering for energy/thermal/mechatronic systems using Modelica.

MLflow Documentation

MLflow

Experiment tracking, model registry, and reproducible ML operations.

Keras Documentation

Keras

High-level deep learning API patterns; pairs well with TensorFlow.

GridLAB-D Documentation

GridLAB-D

Distribution grid simulation for demand response, DERs, and control research.

Microsoft Entra ID Documentation

Microsoft Learn

Identity platform basics: users, groups, apps, SSO, and conditional access.

EdgeX Foundry Documentation

EdgeX Foundry

Open edge platform for IoT/edge systems: device services, data pipelines, and integration.

TRL documentation

Hugging Face

Useful for supervised fine-tuning, preference tuning, and training experiments.