- Your Guide to Linear Regression Models - Oct 5, 2020.
This article explains linear regression and how to program linear regression models in Python.
- Which methods should be used for solving linear regression? - Sep 2, 2020.
As a foundational set of algorithms in any machine learning toolbox, linear regression can be solved with a variety of approaches. Here, we discuss. with with code examples, four methods and demonstrate how they should be used.
- The 8 Basic Statistics Concepts for Data Science - Jun 24, 2020.
Understanding the fundamentals of statistics is a core capability for becoming a Data Scientist. Review these essential ideas that will be pervasive in your work and raise your expertise in the field.
- Linear to Logistic Regression, Explained Step by Step - Mar 3, 2020.
Logistic Regression is a core supervised learning technique for solving classification problems. This article goes beyond its simple code to first understand the concepts behind the approach, and how it all emerges from the more basic technique of Linear Regression.
- Intro to Machine Learning and AI based on high school knowledge - Feb 5, 2020.
Machine learning information is becoming pervasive in the media as well as a core skill in new, important job sectors. Getting started in the field can require learning complex concepts, and this article outlines an approach on how to begin learning about these exciting topics based on high school knowledge.
- How Concerned Should You be About Predictor Collinearity? It Depends… - Aug 15, 2019.
Predictor collinearity (also known as multicollinearity) can be problematic for your regression models. Check out these rules of thumb about when, and when not, to be concerned.
- All Models Are Wrong – What Does It Mean? - Jun 12, 2019.
During your adventures in data science, you may have heard “all models are wrong.” Let’s unpack this famous quote to understand how we can still make models that are useful.
- 3 Main Approaches to Machine Learning Models - Jun 11, 2019.
Machine learning encompasses a vast set of conceptual approaches. We classify the three main algorithmic methods based on mathematical foundations to guide your exploration for developing models.
- KDnuggets™ News 19:n13, Apr 3: Top 10 Data Scientist Coding Mistakes; Explaining Random Forest®; Which Face is Real? - Apr 3, 2019.
Do you know when is using "for" loop a mistake? Read 10 top coding mistakes by Data Scientists; Understand Random Forests and Linear Regression with scikit-learn; Find how to choose the right chart type; and see if you can guess which face is real.
- A Beginner’s Guide to Linear Regression in Python with Scikit-Learn - Mar 29, 2019.
What linear regression is and how it can be implemented for both two variables and multiple variables using Scikit-Learn, which is one of the most popular machine learning libraries for Python.
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- Neural Networks with Numpy for Absolute Beginners — Part 2: Linear Regression - Mar 7, 2019.
In this tutorial, you will learn to implement Linear Regression for prediction using Numpy in detail and also visualize how the algorithm learns epoch by epoch. In addition to this, you will explore two layer Neural Networks.
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- Supervised Learning: Model Popularity from Past to Present - Dec 28, 2018.
An extensive look at the history of machine learning models, using historical data from the number of publications of each type to attempt to answer the question: what is the most popular model?
- KDnuggets™ News 18:n38, Oct 10: Concise Explanation of Learning Algorithms; Why I Call Myself a Data Scientist; Linear Regression in the Wild - Oct 10, 2018.
This week, KDnuggets brings you a discussion of learning algorithms with a hat tip to Tom Mitchell, discusses why you might call yourself a data scientist, explores machine learning in the wild, checks out some top trends in deep learning, shows you how to learn data science if you are low on finances, and puts forth one person's opinion on the top 8 Python machine learning libraries to help get the job done.
- Linear Regression in the Wild - Oct 3, 2018.
We take a look at how to use linear regression when the dependent variables have measurement errors.
- Linear Regression In Real Life - Aug 28, 2018.
A helpful guide to Linear Regression, using an example of a friends road trip to Las Vegas to highlight how it can be used in a real life situation.
- Using Linear Regression for Predictive Modeling in R - Jun 1, 2018.
In this post, we’ll use linear regression to build a model that predicts cherry tree volume from metrics that are much easier for folks who study trees to measure.
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- Using TensorFlow for Predictive Analytics with Linear Regression - Nov 21, 2017.
This post presents a powerful and simple example of how to use TensorFlow to perform a Linear Regression. check out the code for your own experiments!
- You have created your first Linear Regression Model. Have you validated the assumptions? - Nov 15, 2017.
Linear Regression is an excellent starting point for Machine Learning, but it is a common mistake to focus just on the p-values and R-Squared values while determining validity of model. Here we examine the underlying assumptions of a Linear Regression, which need to be validated before applying the model.
- Top 10 Machine Learning Algorithms for Beginners - Oct 20, 2017.
A beginner's introduction to the Top 10 Machine Learning (ML) algorithms, complete with figures and examples for easy understanding.
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- Learn Generalized Linear Models (GLM) using R - Oct 11, 2017.
In this article, we aim to discuss various GLMs that are widely used in the industry. We focus on: a) log-linear regression b) interpreting log-transformations and c) binary logistic regression.
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- Is Regression Analysis Really Machine Learning? - Jun 5, 2017.
What separates "traditional" applied statistics from machine learning? Is statistics the foundation on top of which machine learning is built? Is machine learning a superset of "traditional" statistics? Do these 2 concepts have a third unifying concept in common? So, in that vein... is regression analysis actually a form of machine learning?
- Getting Up Close and Personal with Algorithms - Mar 21, 2017.
We've put together a brief summary of the top algorithms used in predictive analysis, which you can see just below. Read to learn more about Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and more.
- Regression Analysis: A Primer - Feb 6, 2017.
Despite the popularity of Regression, it is also misunderstood. Why? The answer might surprise you: There is no such thing as Regression. Rather, there are a large number of statistical methods that are called Regression, all of which are based on a shared statistical foundation.
- Linear Regression, Least Squares & Matrix Multiplication: A Concise Technical Overview - Nov 24, 2016.
Linear regression is a simple algebraic tool which attempts to find the “best” line fitting 2 or more attributes. Read here to discover the relationship between linear regression, the least squares method, and matrix multiplication.
- Webinar: Modern Regression Modeling for Voter MicroTargeting, Sep 14, Sep 21 - Sep 7, 2016.
Join us for a special 2-part webinar about voting trends, and we will show how machine learning models and data science can be used in elections.
- What is the Role of the Activation Function in a Neural Network? - Aug 30, 2016.
Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.
- A Neat Trick to Increase Robustness of Regression Models - Aug 22, 2016.
Read this take on the validity of choosing a different approach to regression modeling. Why isn't L1 norm used more often?
- The Gentlest Introduction to Tensorflow – Part 1 - Aug 17, 2016.
In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.
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- And the Winner is… Stepwise Regression - Aug 1, 2016.
This post evaluates several methods for automating the feature selection process in large-scale linear regression models and show that for marketing applications the winner is Stepwise regression.