Machine Learning Project Pipeline in Brief
ML projects are combination of multiple algorithms.
Video explains various algorithms working together on various steps of a project.
Stage 1: Data Preparation & Dimension ReductionFunction:This stage cleans, transforms, and compresses massive or messy datasets. It removes noise and merges redundant information so the core model can process the data efficiently without crashing.Types of Algorithms Used:Principal Component Analysis (PCA): Compresses high-dimensional data into fewer columns while retaining key patterns.t-SNE / UMAP: Maps complex data structures down to 2D or 3D planes for grouping.Missing Value Imputation (e.g., KNN Imputer): Automatically fills in missing data blocks based on nearby similar entries.Stage '
2: Core Prediction (The Engine)Function:This is the central mathematical brain of the pipeline. It analyzes the cleaned dataset to find relationships, draw decision boundaries, and make the actual predictions or classifications.Types of Algorithms Used:Linear Models (Logistic / Linear Regression): Draws a straight line or plane to separate data classes or calculate numerical trends.Tree-Based Models (Decision Trees): Splits data step-by-step using a sequence of simple "if/then" rules.Distance-Based Models (K-Nearest Neighbors): Classifies unknown data points based on how close they sit to known data points.Stage
3: Regularization (The Guardrails)Function:This stage applies mathematical penalties to the core model. It prevents the model from "overfitting"—which means memorizing the training data too perfectly and failing when exposed to real-world, unseen data.Types of Algorithms Used:Ridge Regression (L2 Regularization): Shrinks the impact of extreme data points to smooth out the prediction line.Lasso Regression (L1 Regularization): Eliminates useless features entirely by forcing their mathematical weights to absolute zero.Elastic Net: Balances the penalties of both Ridge and Lasso for complex datasets.Stage
4: Ensemble Boosting (The Multiplier)Function:Instead of relying on a single engine, this stage combines multiple individual models to work as a committee, drastically increasing overall accuracy and reducing errors.Types of Algorithms Used:Bagging (Random Forest): Runs hundreds of independent decision trees simultaneously and averages their votes.Boosting (XGBoost / LightGBM): Trains trees sequentially, forcing each new tree to focus exclusively on correcting the mistakes of the previous one.Stacking (Meta-Learning): Uses a final algorithm to blend the predictions of completely different model types.Stage
5: OptimizationFunction:This runs continuously under the hood across the cycle, constantly adjusting the model's inner settings to minimize mistakes and find the path of lowest mathematical error.Types of Algorithms Used:Gradient Descent: Takes iterative steps down an error curve to find the most accurate model weights.Adam / RMSprop: Dynamically speeds up or slows down the learning rate as the optimization process runs.