ML Pipeline - Complete 7 Steps

Click on each tile for specific details

Step 1: Data Preprocessing & Cleaning

Begin by establishing a solid data foundation through comprehensive preprocessing and cleaning.

Data cleaning (inconsistencies, duplicates, noise)
Missing value imputation - must be done first
Outlier detection & treatment - early identification crucial
Data integration & transformation (aggregation, generalization)

Step 2: Feature Engineering

Transform and optimize features to maximize model performance and interpretability.

Categorical encoding - transform categorical variables
Numerical scaling - normalize/standardize features
Feature creation - generate new meaningful features

Step 3: Feature Selection

Select the most relevant features to improve model performance and reduce complexity.

Filter methods - quick elimination of irrelevant features
Wrapper/embedded methods - model-based selection
Correlation analysis - remove redundant features

Step 4: Model Selection

Choose appropriate algorithms and optimize hyperparameters for best performance.

Algorithm selection - choosing the right model type
Hyperparameter tuning - optimizing model parameters
Cross-validation strategies - robust evaluation setup

Step 5: Model Training & Evaluation

Train models and evaluate their performance using appropriate metrics and techniques.

Data splitting - preparing data for training and testing
Model fitting - training the chosen model
Ensemble methods - combining models for better performance
Model evaluation - using metrics to assess performance
Overfitting/underfitting diagnosis - identifying model issues

Step 6: Regularization Strategies

Apply regularization techniques to prevent overfitting and improve generalization.

Applied during model training - prevent overfitting
Hyperparameter tuning - optimize regularization strength

Step 7: Dimensionality Reduction

Reduce feature dimensions when necessary for computational efficiency and visualization.

Used when necessary - high-dimensional data, visualization
Computational efficiency - reduce training time

Next Step: The Deep Learning Pipeline

While the classical pipeline is powerful for structured data, tasks involving images, text, or complex sequences often require the power of neural networks. The Deep Learning Pipeline follows a different set of specialized steps.

Explore the end-to-end workflow for building, training, and deploying deep learning models.
Computational efficiency - reduce training time