In this article we explore SVM for regression and classification and use ML.NET to implement it.
In a previous couple of articles, we explored some basic machine learning algorithms and how they fit into the .NET world. Thus far we covered some simple regression algorithms, classification algorithms. Apart from that, we learned a bit about unsupervised learning, more specifically – clustering. We used ML.NET to implement and apply these algorithms. In this article, we explore one of the most popular machine learning algorithms Support Vector Machine or SVM for short.
In this article, we explore gradient descent - the grandfather of all optimization techniques and it’s variations. We implement them from scratch with Python.
What is the difference between machine learning and artificial intelligence and deep learning? Supervised learning is best for classification and regressions Machine Learning models. You can read more about them in this article.
In this article, we explore Linear regression, we implement it from scratch with C# and implement it with ML.NET.
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