Machine Learning with Scikit-learn
All Courses
Data Science
intermediate

Machine Learning with Scikit-learn

4.9
8,151 students
3h 45m
15 lessons

About this course

Learn practical machine learning with Python and scikit-learn. Build a complete ML workflow — data preprocessing, feature engineering, model selection, and evaluation — then master regression, classification, clustering, and dimensionality reduction. Includes end-to-end projects on real datasets with a focus on production-ready pipelines.

Course Content

The ML Workflow

  • Data Preprocessing and Feature Scaling15min
  • Evaluation Metrics: Accuracy, Precision, Recall15min
  • Supervised vs Unsupervised Learning15min
  • Train/Test Splits and Cross-Validation15min

Supervised Models

  • Decision Trees and Random Forests15min
  • Gradient Boosting (XGBoost and LightGBM)15min
  • Linear and Logistic Regression15min
  • Support Vector Machines15min

Unsupervised Learning

  • Principal Component Analysis (PCA)15min
  • K-Means and Hierarchical Clustering15min
  • Feature Engineering and Selection15min

Production Pipelines

  • Scikit-learn Pipelines and ColumnTransformers15min
  • Project: End-to-End Churn Prediction15min
  • Hyperparameter Tuning with Grid and Random Search15min
  • Model Persistence and Serving15min