# Archive | Machine Learning Algorithms ## Gradient Descent For Machine Learning

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: […] ## Overfitting and Underfitting With Machine Learning Algorithms

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. Let’s get started. Approximate a Target Function in Machine Learning Supervised machine learning is best understood as […] ## Gentle Introduction to the Bias-Variance Trade-Off in Machine Learning

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. Let’s get started. Update Oct/2019: Removed discussion of parametric/nonparametric models (thanks Alex). Overview […] ## Supervised and Unsupervised Machine Learning Algorithms

What is supervised machine learning and how does it relate to unsupervised machine learning? In this post you will discover supervised learning, unsupervised learning and semis-supervised learning. After reading this post you will know: About the classification and regression supervised learning problems. About the clustering and association unsupervised learning problems. Example algorithms used for supervised and […] ## Parametric and Nonparametric Machine Learning Algorithms

What is a parametric machine learning algorithm and how is it different from a nonparametric machine learning algorithm? In this post you will discover the difference between parametric and nonparametric machine learning algorithms. Let’s get started. Learning a Function Machine learning can be summarized as learning a function (f) that maps input variables (X) to output […] ## How Machine Learning Algorithms Work (they learn a mapping of input to output)

How do machine learning algorithms work? There is a common principle that underlies all supervised machine learning algorithms for predictive modeling. In this post you will discover how machine learning algorithms actually work by understanding the common principle that underlies all algorithms. Le’s get started. Let’s get started. Learning a Function Machine learning algorithms are […] ## 5 Ways To Understand Machine Learning Algorithms (without math)

Where does theory fit into a top-down approach to studying machine learning? In the traditional approach to teaching machine learning, theory comes first requiring an extensive background in mathematics to be able to understand it. In my approach to teaching machine learning, I start with teaching you how to work problems end-to-end and deliver results. […] ## Use Random Forest: Testing 179 Classifiers on 121 Datasets

If you don’t know what algorithm to use on your problem, try a few. Alternatively, you could just try Random Forest and maybe a Gaussian SVM. In a recent study these two algorithms were demonstrated to be the most effective when raced against nearly 200 other algorithms averaged over more than 100 data sets. In […] ## Better Naive Bayes: 12 Tips To Get The Most From The Naive Bayes Algorithm

Naive Bayes is a simple and powerful technique that you should be testing and using on your classification problems. It is simple to understand, gives good results and is fast to build a model and make predictions. For these reasons alone you should take a closer look at the algorithm. In a recent blog post, you […] ## Take Control By Creating Targeted Lists of Machine Learning Algorithms

Any book on machine learning will list and describe dozens of machine learning algorithms. Once you start using tools and libraries you will discover dozens more. This can really wear you down, if you think you need to know about every possible algorithm out there. A simple trick to tackle this feeling and take some […]