Last Updated on October 25, 2019 Supervised machine learning algorithms can best be understood through the lens of the bias-variance trade-off. In this post, you will discover the Bias-Variance Trade-Off and how to use it to better understand machine learning algorithms and get better performance on your data. Kick-start your project with my new book Master Machine Learning Algorithms, including step-by-step tutorials and the Excel Spreadsheet files for all examples. Let’s get started. Update Oct/2019: Removed discussion of parametric/nonparametric models […]

Last Updated on August 12, 2019 The cause of poor performance in machine learning is either overfitting or underfitting the data. In this post, you will discover the concept of generalization in machine learning and the problems of overfitting and underfitting that go along with it. Kick-start your project with my new book Master Machine Learning Algorithms, including step-by-step tutorials and the Excel Spreadsheet files for all examples. Let’s get started. Overfitting and Underfitting With Machine Learning AlgorithmsPhoto by Ian Carroll, some […]

Last Updated on August 12, 2019 Optimization is a big part of machine learning. Almost every machine learning algorithm has an optimization algorithm at it’s core. In this post you will discover a simple optimization algorithm that you can use with any machine learning algorithm. It is easy to understand and easy to implement. After reading this post you will know: What is gradient descent? How can gradient descent be used in algorithms like linear regression? How can gradient descent […]

Last Updated on August 15, 2020 Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning. In this post you will discover the linear regression algorithm, how it works and how you can best use it in on your machine learning projects. In this post you will learn: Why linear regression belongs to both statistics and machine learning. The many names by which linear regression is known. The representation and learning […]

Last Updated on August 12, 2019 Linear regression is a very simple method but has proven to be very useful for a large number of situations. In this post, you will discover exactly how linear regression works step-by-step. After reading this post you will know: How to calculate a simple linear regression step-by-step. How to perform all of the calculations using a spreadsheet. How to make predictions on new data using your the model. A shortcut that greatly simplifies the calculation. […]

Last Updated on August 12, 2019 Stochastic Gradient Descent is an important and widely used algorithm in machine learning. In this post you will discover how to use Stochastic Gradient Descent to learn the coefficients for a simple linear regression model by minimizing the error on a training dataset. After reading this post you will know: The form of the Simple Linear Regression model. The difference between gradient descent and stochastic gradient descent How to use stochastic gradient descent to […]

Last Updated on August 15, 2020 Logistic regression is another technique borrowed by machine learning from the field of statistics. It is the go-to method for binary classification problems (problems with two class values). In this post you will discover the logistic regression algorithm for machine learning. After reading this post you will know: The many names and terms used when describing logistic regression (like log odds and logit). The representation used for a logistic regression model. Techniques used to […]

Last Updated on August 12, 2019 Logistic regression is one of the most popular machine learning algorithms for binary classification. This is because it is a simple algorithm that performs very well on a wide range of problems. In this post you are going to discover the logistic regression algorithm for binary classification, step-by-step. After reading this post you will know: How to calculate the logistic function. How to learn the coefficients for a logistic regression model using stochastic gradient […]

Last Updated on August 15, 2020 Logistic regression is a classification algorithm traditionally limited to only two-class classification problems. If you have more than two classes then Linear Discriminant Analysis is the preferred linear classification technique. In this post you will discover the Linear Discriminant Analysis (LDA) algorithm for classification predictive modeling problems. After reading this post you will know: The limitations of logistic regression and the need for linear discriminant analysis. The representation of the model that is learned […]