What you'll learn

Skills you'll gain

There are 14 modules in this course

Next, you will learn model preparation and evaluation, focusing on underfitting, overfitting, data splitting, and resampling methods, alongside regularization techniques to enhance model performance. The course covers classification methods, including confusion matrices, ROC curves, decision trees, random forests, logistic regression, and support vector machines, all paired with practical labs. You will also explore ensemble models and association rules, like the Apriori algorithm, to uncover hidden data patterns. Designed for data scientists, machine learning enthusiasts, and technical professionals, this course requires a basic understanding of machine learning concepts and Python programming. Learning outcomes include grasping AI and machine learning fundamentals, applying regression analysis, building and evaluating models, implementing classification techniques, performing clustering and dimensionality reduction, uncovering patterns with association rules, and applying reinforcement learning principles.

Machine Learning: Regression

Machine Learning: Model Preparation and Evaluation

Machine Learning: Regularization

Machine Learning: Classification Basics

Machine Learning: Classification with Decision Trees

Machine Learning: Classification with Random Forests

Machine Learning: Classification with Logistic Regression

Machine Learning: Classification with Support Vector Machines

Machine Learning: Classification with Ensemble Models

Machine Learning: Association Rules

Machine Learning: Clustering

Machine Learning: Dimensionality Reduction

Machine Learning: Reinforcement Learning

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