AI & Machine Learning Hub

Mastering Scikit-learn: Practical Tutorials for Real-World Applications

Introduction to Scikit-learn

Introduction to Scikit-learn

Get started with the fundamental concepts of Scikit-learn. Learn about its core modules, common estimators, and the basic workflow for machine learning tasks.

Beginner Fundamentals Data Science
Supervised Learning with Scikit-learn

Supervised Learning with Scikit-learn

Explore key supervised learning algorithms like Linear Regression, Logistic Regression, Support Vector Machines, and Decision Trees. Understand their applications and implementation.

Intermediate Algorithms Classification Regression
Unsupervised Learning with Scikit-learn

Unsupervised Learning with Scikit-learn

Dive into unsupervised learning techniques such as K-Means Clustering, PCA for dimensionality reduction, and DBSCAN. Discover patterns in data without predefined labels.

Intermediate Clustering Dimensionality Reduction
Model Evaluation & Tuning

Model Evaluation & Tuning

Learn how to effectively evaluate the performance of your machine learning models using metrics like accuracy, precision, recall, and F1-score. Master techniques for hyperparameter tuning.

Intermediate Evaluation Tuning Cross-Validation
Feature Engineering Techniques

Feature Engineering Techniques

Understand the critical role of feature engineering in machine learning. Explore methods for data preprocessing, feature scaling, encoding categorical variables, and creating new features.

Intermediate Preprocessing Data Transformation
Advanced Scikit-learn Topics

Advanced Scikit-learn Topics

Explore more complex topics like ensemble methods (Random Forests, Gradient Boosting), pipeline creation for streamlined workflows, and model deployment considerations.

Advanced Ensembles Pipelines