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Machine Learning Intermediate

Scikit-learn Basics

A COMPLETE beginner-to-advanced Machine Learning course. Learn supervised/unsupervised learning, modeling, pipelines, and deployment using Scikit-learn.

20 chapters 1h 58m 4.6 (131)
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What you'll learn

  • Introduction to Machine Learning and Scikit-learn
  • Setting Up Python and Scikit-learn Environment
  • Python Basics for Machine Learning
  • NumPy and Pandas Essentials
  • Understanding Machine Learning Workflow
  • Data Preprocessing and Cleaning
  • Feature Engineering and Encoding
  • Train-Test Split and Cross Validation

Course content

20 chapters · 1h 58m
  1. 1 Introduction to Machine Learning and Scikit-learn 7 min
  2. 2 Setting Up Python and Scikit-learn Environment 6 min
  3. 3 Python Basics for Machine Learning 6 min
  4. 4 NumPy and Pandas Essentials 5 min
  5. 5 Understanding Machine Learning Workflow 6 min
  6. 6 Data Preprocessing and Cleaning 6 min
  7. 7 Feature Engineering and Encoding 6 min
  8. 8 Train-Test Split and Cross Validation 7 min
  9. 9 Linear Regression in Scikit-learn 6 min
  10. 10 Logistic Regression for Classification 5 min
  11. 11 Decision Trees and Random Forests 6 min
  12. 12 Support Vector Machines (SVM) 6 min
  13. 13 K-Nearest Neighbors (KNN) 6 min
  14. 14 Clustering with K-Means 6 min
  15. 15 Dimensionality Reduction with PCA 6 min
  16. 16 Model Evaluation Metrics 6 min
  17. 17 Hyperparameter Tuning and GridSearchCV 5 min
  18. 18 Building ML Pipelines in Scikit-learn 6 min
  19. 19 Saving and Deploying Machine Learning Models 6 min
  20. 20 Final Project - Build Complete Machine Learning Applications 5 min