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Machine Learning Intermediate

Classification Algorithms

A COMPLETE beginner-to-advanced Machine Learning course on Classification Algorithms using Python and Scikit-learn. Learn Logistic Regression, Random Forests, SVMs, and more.

20 chapters 1h 55m 4.9 (170)
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What you'll learn

  • Introduction to Classification Algorithms
  • Setting Up Python and Machine Learning Environment
  • Python Basics for Machine Learning
  • NumPy, Pandas, and Data Preparation
  • Understanding Classification Fundamentals
  • Logistic Regression for Classification
  • K-Nearest Neighbors (KNN)
  • Decision Tree Classification

Course content

20 chapters Β· 1h 55m
  1. 1 Introduction to Classification Algorithms 6 min
  2. 2 Setting Up Python and Machine Learning Environment 6 min
  3. 3 Python Basics for Machine Learning 6 min
  4. 4 NumPy, Pandas, and Data Preparation 6 min
  5. 5 Understanding Classification Fundamentals 6 min
  6. 6 Logistic Regression for Classification 6 min
  7. 7 K-Nearest Neighbors (KNN) 6 min
  8. 8 Decision Tree Classification 5 min
  9. 9 Random Forest Classification 6 min
  10. 10 Support Vector Machines (SVM) 5 min
  11. 11 Naive Bayes Classification 6 min
  12. 12 Ensemble Learning and Boosting 6 min
  13. 13 Feature Engineering and Data Preprocessing 5 min
  14. 14 Handling Imbalanced Datasets 6 min
  15. 15 Multiclass and Multilabel Classification 6 min
  16. 16 Model Evaluation Metrics for Classification 5 min
  17. 17 Hyperparameter Tuning and Cross Validation 6 min
  18. 18 Building Classification Pipelines 6 min
  19. 19 Saving, Deploying, and Using Classification Models 6 min
  20. 20 Final Project - Build Real-World Classification Applications 5 min