Coursera MOOC / Non-credit 0

Intermediate Data Manipulation and Machine Learning

Name: Intermediate Data Manipulation and Machine Learning
Price: 49 USD
Availability: InStock

About this course

Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. 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.

74/100

CourseAsk score

What the provider tells you: 38/45
Who stands behind it: 20/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

grasp AI and machine learning fundamentals
apply regression analysis
build and evaluate models
implement classification techniques
perform clustering and dimensionality reduction
uncover patterns with association rules
apply reinforcement learning principles

Machine Learning Data Analysis #reinforcement learning #python #classification #machine learning #model evaluation #regression analysis #dimensionality reduction #clustering #logistic regression #decision trees #random forests #support vector machines #association rules #Apriori algorithm

$49.00

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