⚡ Production-Grade ML Reference
ML Engineering
Reference Hub
The definitive coding reference for Machine Learning engineers. Deep-dive documentation covering syntax, parameters, internals, edge cases, and performance—not theory.
7
Libraries Covered
60+
Functions Documented
100+
Code Examples
0
Basic Theory
NumPy
Complete ML-relevant NumPy API — arrays, broadcasting, linear algebra,
random sampling, and memory layout.
ndarray
linalg
random
broadcasting
vectorization
Pandas
ML-focused Pandas — data loading, cleaning, merging, grouping, indexing,
view vs copy pitfalls, and performance.
DataFrame
Series
groupby
merge
loc/iloc
Matplotlib
Full Matplotlib documentation with advanced customization, subplots,
styles, and ML visualization patterns.
pyplot
scatter
subplots
hist
imshow
Sklearn — Preprocessing
train_test_split, Scalers, Encoders, Imputers, ColumnTransformer,
Pipeline — with internals and edge cases.
StandardScaler
Imputer
Pipeline
Encoders
Sklearn — Models
LinearRegression, LogisticRegression, Trees, Forests, GradientBoosting,
SVM, KNN, KMeans, DBSCAN — parameters & internals.
LinearReg
RandomForest
SVC
KMeans
Sklearn — Evaluation
accuracy, precision, recall, F1, confusion matrix, ROC-AUC,
cross-validation, GridSearchCV, RandomizedSearchCV.
GridSearchCV
cross_val
ROC-AUC
F1
TensorFlow / Keras
Sequential, compile, fit, evaluate, predict, callbacks, tf.data API,
custom training loops — advanced coding focus.
Sequential
compile/fit
callbacks
tf.data
ML Pipelines
Production-ready end-to-end ML pipelines combining Pandas → Preprocessing
→ Splitting → Scaling → Model → Evaluation.
Pipeline
ColumnTransformer
Data Leakage
Advanced Topics
Random state theory, overfitting/underfitting debugging, feature
importance, saving models, deployment basics.
joblib
pickle
overfitting
deployment
📘 What This Reference Covers
Every function follows a strict 7-section documentation format
Function Signature
Full syntax with all parameters
Parameter Table
Type, default, edge cases for each param
Internal Working
How it works under the hood
Practical Examples
Real dataset code you can copy-paste
Common Mistakes
Pitfalls and bugs to avoid
Performance Notes
Memory, speed, large dataset behavior