Supervised Learning
Master the most common type of machine learning. Learn to build models that make predictions based on labeled data, covering regression and classification tasks.
59 courses
Master foundational machine learning by building, evaluating, and interpreting linear regression models in Python to solve real-world business problems.
Build and evaluate robust classification models using SVM and kernel methods for real-world data analysis.
Build a strong foundation for machine learning and deep learning by mastering linear regression theory and Python implementation from scratch.
Build and explain predictive models using linear and non-linear regression, feature selection, and modern interpretability tools like SHAP and LIME.
Build a strong foundation in predictive modeling by writing clean Python code and implementing classic machine learning algorithms to solve real-world problems.
A practical guide for data professionals to build predictive models using only the SQL they already know.
Learn to build, tune, and evaluate classification models in Python, from logistic regression to ensemble methods, using real-world data science workflows.
Build, evaluate, and optimize predictive models using Python and scikit-learn through structured, step-by-step written guides.
Build, tune, and evaluate predictive models using Python and scikit-learn to solve real-world classification and regression problems.
Build a solid foundation in predictive modeling by understanding the core algorithms and mathematical principles behind supervised machine learning.
Build a solid foundation in predictive modeling and data patterns to solve practical problems using modern machine learning techniques.
Build and apply reliable prediction models to solve real-world data challenges using modern algorithmic techniques.
Master the fundamentals of regression and classification to build your first predictive models in Python.
Master the fundamentals of classification to build predictive models for spam detection, sentiment analysis, and data-driven decision making.
Learn to predict continuous numerical outcomes and evaluate model accuracy using modern data science workflows and best practices.
Learn to frame real-world machine learning problems, prepare datasets using modern workflows, and design practical solutions for business, finance, and engineering.
Learn to build, tune, and evaluate powerful classification and regression models using Python and scikit-learn to solve real-world data challenges.
Build and scale machine learning models for large datasets using PySpark, from data preparation and regression to decision trees and pipeline automation.
Build and evaluate predictive models to categorize data accurately using modern industry-standard techniques and performance metrics.
Master the core principles of linear regression and build your first predictive models using Python to uncover data-driven insights.
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