In this comprehensive course, you will explore artificial intelligence (AI) and its core concepts, forming a solid foundation for machine learning. You will delve into regression analysis, applying univariate, polynomial, and multivariate regression techniques to real-world problems through interactive labs. 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.
Confusion Matrix 101
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Skills You'll Learn
data manipulation, Regression Analysis, Clustering, Machine Learning, Python programming
From the lesson
Machine Learning: Classification Basics
In this module, we will cover the basics of classification. We will start with confusion matrices and ROC curves, then engage in interactive and lab sessions to gain hands-on experience in evaluating and optimizing classification models.
Taught By
Packt - Course Instructors