Lecture 7

What is k-Means Clustering?

K-means clustering is an unsupervised learning algorithm used for data clustering, which groups unlabeled data points into groups or clusters.

It is one of the most popular clustering methods used in machine learning. Unlike supervised learning, the training data that this algorithm uses is unlabeled, meaning that data points do not have a defined classification structure.

While various types of clustering algorithms exist, including exclusive, overlapping, hierarchical and probabilistic, the k-means clustering algorithm is an example of an exclusive or “hard” clustering method. This form of grouping stipulates that a data point can exist in just one cluster. This type of cluster analysis is commonly used in data science for market segmentation, document clustering, image segmentation and image compression. The k-means algorithm is a widely used method in cluster analysis because it is efficient, effective and simple.

Train Test Validation Split: How to & Best Practices

For training and testing purposes of our model, we should have our data broken down into three distinct dataset splits. The Training set is the set of data that is used to train and make the model learn the hidden features/patterns in the data.

In each epoch, the same training data is fed to the neural network architecture repeatedly, and the model continues to learn the features of the data.

The training set should have a diversified set of inputs so that the model is trained in all scenarios and can predict any unseen data sample that may appear in the future.

K-Means Clustering Example

K-means clustering is a popular method for grouping data by assigning observations to clusters based on proximity to the cluster’s center. This article explores k-means clustering, its importance, applications, and workings, providing a clear understanding of its role in data analysis. In this article, you will explore k-means clustering, an unsupervised learning technique that groups data points into clusters based on similarity. A k means clustering example illustrates how this method assigns data points to the nearest centroid, refining the clusters iteratively. Understanding what is k-means clustering will enhance your grasp of data analysis and pattern recognition.read more

The Confusion Matrix: What you need to understand before training your prediction model

In high-stakes engineering applications like battery fault detection, precision is everything. But how do you consistently achieve it? The confusion matrix holds the key.

In this blog, we’ll explore how this essential tool can sharpen your model’s accuracy and guide you toward data-driven decisions that boost business outcomes. Ready to elevate your predictive performance? Let’s get started.

A confusion matrix is a simple yet powerful tool used to evaluate the performance of a classification model. In its most basic form (binary classification), the matrix is a 2x2 table comparing actual and predicted outcomes.

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Train Test Split: What It Means and How to Use It

A goal of supervised learning is to build a machine learning model that performs well on new data. If you have new data, it’s a good idea to see how your model performs on it. The problem is that you may not have new data, but you can simulate this experience with a procedure like train test split.
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Which kth model to choose after k-fold Cross Validation?This might sound silly, but when I was first self-learning the concepts in Machine Learning, it took me a while to wrap my head around Cross Validation. I was so confused at first because I couldn’t understand the difference between Validation data and Test data. Looking back, I feel a little dumb for thinking that, but at the same time, I have to acknowledge that it is in-fact a little confusing. So through this article, I want to dissect the concept and explain the purpose of Cross Validation, as well as, finally give an answer to the question — “Which kth model/surrogate to choose after k-fold Cross Validation?” But to do that, let me give a little bit of background on what Machine Learning is.

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