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Machine Learning/Learning Center/Linear Models

Linear Models

Master the foundation of machine learning with linear models. Learn regression, classification, and advanced techniques for real-world applications from housing prices to fraud detection.

Linear Models Overview
Module 1
Understand the fundamentals of linear models, their mathematical foundations, advantages, disadvantages, and real-world applications. Learn about extensions like regularization and kernel methods.

Topics Covered:

Linear Model Definition
Advantages & Disadvantages
Model Interpretability
Applications & Extensions
Ridge & Lasso Regularization
Linear Regression
Module 2
Master least squares method for regression tasks. Learn single and multiple variable regression with housing price prediction examples, OLS assumptions, and log-linear models.

Topics Covered:

Least Squares Method
Single & Multiple Variable Regression
Ordinary Least Squares (OLS)
Log-Linear Regression
Housing Price Prediction
Logistic Regression
Module 3
Learn binary classification with logistic regression. Understand sigmoid functions, maximum likelihood estimation, and apply it to credit approval and medical diagnosis problems.

Topics Covered:

Sigmoid Function & Log-Odds
Binary Classification
Maximum Likelihood Estimation
Gradient Descent Solution
Credit Approval Example
Linear Discriminant Analysis
Module 4
Explore LDA for classification tasks. Learn projection optimization, scatter matrices, and understand when to use LDA versus logistic regression with customer segmentation examples.

Topics Covered:

LDA Theory & Optimization
Within-Class & Between-Class Scatter
Gaussian Distribution Assumptions
LDA vs Logistic Regression
Customer Segmentation
Multi-Class Classification
Module 5
Learn strategies for extending binary classifiers to multi-class problems. Master One-vs-One, One-vs-Rest, and Many-vs-Many approaches with practical e-commerce examples.

Topics Covered:

One-vs-One (OvO) Strategy
One-vs-Rest (OvR) Strategy
Many-vs-Many (MvM) & ECOC
Strategy Comparison
Product Categorization
Class Imbalance Problems
Module 6
Address class imbalance in classification tasks. Learn undersampling, oversampling (SMOTE), threshold moving, and apply techniques to fraud detection and disease screening.

Topics Covered:

Class Imbalance Definition
Undersampling Techniques
Oversampling & SMOTE
Threshold Moving
Fraud Detection Example

Suggested Learning Paths

Regression Path

Focus on predicting continuous values

  • Overview
  • Linear Regression
  • Regularization

Classification Path

Focus on predicting discrete classes

  • Overview
  • Logistic Regression
  • LDA
  • Multi-Class

Advanced Path

Handle complex real-world scenarios

  • All Topics
  • Class Imbalance
  • Feature Engineering

Why Learn Linear Models?

Foundation for Advanced Models

Linear models form the basis for understanding neural networks, SVMs, and other advanced algorithms. Master these fundamentals first.

Interpretable Results

Linear models provide clear, interpretable coefficients that explain feature importance—crucial for business decisions and regulated industries.

Fast & Efficient

Linear models train quickly and scale well to large datasets, making them ideal for production environments and real-time applications.

Industry Standard Baseline

Used by data scientists worldwide as baseline models before trying complex approaches. Essential for any ML practitioner's toolkit.

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