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Regression & Modeling

12 resources

Level
Type

📹Videos(1)

video👁️ 0

StatQuest: Linear Regression Clearly Explained

Josh Starmer explains linear regression fundamentals

beginner🇬🇧

🎓Courses(4)

course👁️ 0

Supervised Machine Learning: Regression and Classification

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.

beginner🇬🇧
course👁️ 0

Regression Models

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

intermediate🇬🇧
course👁️ 0

Statistical Learning

Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in R. Course material updated in 2021 for second edition of the course textbook.

intermediate🇬🇧
course👁️ 0

Regression Models

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

beginner🇬🇧

📚Books(2)

book👁️ 0

An Introduction to Statistical Learning

An Introduction to Statistical Learning - A book resource

intermediate🇬🇧
book👁️ 0

Applied Regression Analysis

Applied Regression Analysis - A book resource

intermediate🇬🇧

🌐Websites(5)

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website👁️ 0

Scikit-learn User Guide: Linear Models

Practical regression models in Python

beginner🇬🇧
website👁️ 0

The Elements of Statistical Learning

Advanced free textbook on statistical modeling

beginner🇬🇧
website👁️ 0

An Introduction to Statistical Learning

Free book and resources for regression and modeling

beginner🇬🇧
website👁️ 0

statmodeling.stat.columbia.edu

Stat Modeling, Causal Inference, and Social Science is a blog and resource hub focused on statistical modeling and regression analysis, including Bayesian methods, causal inference, and practical data analysis in social science. It features accessible explanations, tutorials, and discussions on modeling techniques, data pitfalls, and real-world applications.

intermediate🇬🇧
website👁️ 0

islp-book.com

Online textbook for statistical learning with a focus on regression and modeling. It covers linear and generalized linear models, regularization, resampling/validation, tree-based methods, and includes practical code-based examples.

intermediate🇬🇧

Frequently Asked Questions

What are the best free resources to learn Regression & Modeling?

Dantes has curated 8 resources for Regression & Modeling, including 1 videos, 4 courses, 3 websites. All resources are hand-picked for quality — no algorithmic filler. Browse the full list above to find the format that works best for you.

Is Regression & Modeling hard to learn?

Regression & Modeling is approachable at the beginner level — there are resources here specifically for those starting from scratch. As you progress, intermediate and advanced material is also available to take your skills further.

What types of Regression & Modeling learning resources are available on Dantes?

For Regression & Modeling, Dantes has curated 1 videos, 4 courses, 3 websites. Each resource type serves a different learning style: videos and YouTube for visual learners, books for depth, courses for structured progression, and websites for quick reference.

How does Dantes select Regression & Modeling resources?

Dantes is an algorithm-free learning directory. Resources are hand-curated based on quality, accuracy, and usefulness — not engagement metrics or paid placements. The goal is to surface the best learning material for Regression & Modeling, whether it's a free YouTube series, a classic textbook, or an open courseware from a top university.

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