Highlights: Hello and welcome. In the previous posts, we talked in great detail about supervised learning. In this post, we’ll talk about unsupervised learning. In particular, you’ll learn about clustering algorithms, which is a way of grouping data into clusters. In particular, we will talk about one of the most popular clustering algorithms called k-means clustering.  Tutorial overview: What is clustering? K-means algorithm K-means algorithm in Python 1. What is clustering? A clustering algorithm looks…
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Highlights: Hello and welcome. By now we have learned about different learning algorithms, including linear regression and logistic regression. You already have a lot of powerful tools for machine learning, but how do you use these tools effectively? The efficiency of how quickly you can get a machine learning system to work well will depend to a large part on how well you can repeatedly make good decisions about what to do next. In this…
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Highlights: Hello and welcome. So far we learned what binary classification is and we looked at one of the most common binary classification algorithms called logistic regression. In today’s post, we are going to cover another type of classification problem called multiclass classification. It refers to classification problems with more than just two possible output labels. not just 0 or 1. Let’s begin with our post and dive deeper into this multiclass classification.  Tutorial overview:…
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Highlights: Hello and welcome. In today’s post, we are going to talk about one of the learning algorithms that are very powerful and is used in many machine learning applications. It is called decision trees and tree ensembles. It is a very powerful tool that is well worth having in your toolbox. In this post, we’ll learn about decision trees and we’ll see how you can apply them in your own machine learning projects. So,…
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Highlights: In the previous post we learned to build a simple Neural Network. Recall that we’ve been using the sigmoid activation function in all the nodes in the hidden layers and in the output layer. The reason for that is we were building up neural networks using Logistic regression algorithms. However, if we apply other types of activation functions, our neural network can become much more powerful. In this post, we are going to explore…
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