Learn the basics of machine learning, explore Scikit-learn's functionality, and understand its role in data analysis and AI development. This module covers essential machine learning terminology and techniques to get you started.

Master data preprocessing techniques, including handling missing values, scaling data, encoding categorical variables, and splitting data into training and testing sets to prepare it for modeling.

Dive into classification algorithms such as Logistic Regression, Decision Trees, and k-Nearest Neighbors (k-NN). Learn how to implement these models, evaluate their performance, and fine-tune them for optimal results.

Explore regression algorithms such as Linear Regression, Polynomial Regression, and Decision Trees for regression tasks. Learn how to assess the performance of regression models and address common challenges like overfitting.

Understand the importance of model selection and how to use techniques like cross-validation and grid search to fine-tune the hyperparameters of your models for better performance.

Learn about unsupervised learning techniques like clustering (K-means) and dimensionality reduction (PCA). This module helps you understand how to extract insights from unlabelled data.

Discover how ensemble methods like Random Forests, Boosting, and Bagging can be used to combine multiple models and improve the accuracy of predictions.

Learn how to deploy machine learning models in real-world applications, using Scikit-learn’s tools to save, load, and make predictions with trained models. This module covers best practices for integrating machine learning models into production environments.