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Machine Learning and AI Foundations: Classification Modeling

Offered By: LinkedIn Learning

Tags

Machine Learning Courses Artificial Intelligence Courses Logistic Regression Courses Decision Trees Courses Binary Classification Courses

Course Description

Overview

Classification methods are among the most important in modern data science. Learn classification strategies and algorithms for machining learning and AI.

Syllabus

Introduction
  • Classification problems in machine learning
  • What you should know
  • Defining terms
1. The Big Picture: Defining Your Classification Strategy
  • The importance of binary classification
  • Binary vs. multinomial
  • So-called “black box” techniques
  • One task, many algorithms
  • Statistics vs. machine learning
  • Model assessment vs. business evaluation
2. How Do I Choose a "Winner"?
  • Training and test partitions
  • Lift Charts
  • Gains tables
  • Confusion matrix
3. Algorithms on Parade
  • Overview
  • Discriminant with three categories
  • Discriminant with two categories
  • Stepwise discriminant
  • Logistic regression
  • Stepwise logistic regression
  • Decision Trees
  • KNN
  • Linear SVM
  • Neural nets
  • Bayesian networks
  • Heterogenous ensembles
  • Bagging and random forest
  • Boosting and XGBoost
4. Common Modeling Challenges
  • Imbalanced target categories
  • Interactions
  • Missing data
  • Bias-variance trade-off and overfitting
  • Data reduction
  • AutoML
Conclusion
  • Next steps

Taught by

Keith McCormick

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