Different type of models used in Machine       Learning in Python 

Scikit-Learn (Traditional Machine Learning)

LinearRegression: Basic regression for continuous data.
LogisticRegression: Standard baseline for binary classification.
RandomForestClassifier / RandomForestRegressor: Powerful ensemble tree models.
GradientBoostingClassifier: Sequential tree building for high accuracy.
SVC: Support Vector Classifier for complex boundaries.
KMeans: Unsupervised clustering algorithm.

XGBoost & LightGBM (Gradient Boosting)

XGBClassifier / XGBRegressor: High-performance, scalable gradient boosting.
LGBMClassifier / LGBMRegressor: Fast, leaf-wise tree growth models.

PyTorch (Deep Learning Ecosystem)

torchvision.models.resnet50: Industry standard for image classification.
torchvision.models.vit_b_16: Vision Transformer for advanced image tasks.
torchaudio.models.wave2vec2_model: Architecture for speech processing.
torch.nn.Transformer: Raw building block for sequence-to-sequence tasks.

TensorFlow / Keras (Deep Learning Ecosystem)

keras.applications.ResNet50: Pre-trained deep residual network for vision.
keras.applications.MobileNetV3Large: Lightweight, mobile-optimized vision model.
keras.applications.EfficientNetB0: State-of-the-art scaling for image tasks.
keras.layers.LSTM: Recurrent layer stringed together for text/time-series.

Hugging Face Transformers (NLP & GenAI)

bert-base-uncased: Baseline for text classification and extraction.
roberta-base: Improved, harder-trained version of BERT.
gpt2: Standard starting point for causal text generation.
meta-llama/Meta-Llama-3-8B: State-of-the-art large language model for fine-tuning.
google/vit-base-patch16-224: Vision transformer adapted for Hugging Face pipelines. 

Statsmodels (Statistical & Time Series)

OLS: Ordinary Least Squares regression with deep statistical summaries.
ARIMA: Autoregressive Integrated Moving Average for time-series forecasting.
Logit: Logistic regression specialized for statistical inference.