In this article, we will take a practical approach to the k-Nearest Neighbor in machine learning. For implementation purposes of the k-Nearest Neighbor, we will use the Scikit-Learn library. We will try a classification problem using KNN. We will try the digit classification using the MNIST dataset. After all, before neural networks, traditional machine learning […] ...

In machine learning, there are many traditional algorithms that will remain relevant for a long time. k-Nearest Neighbor (KNN) is one such algorithm that we will get to know in this article. KNN is a simple and efficient algorithm. It is easy to understand the methodology of KNN as well. In this article, we will […] ...

In machine learning, we seldom get datasets that are perfect from the beginning. Almost it never happens that we get the data and just feed into our machine learning algorithms. In fact, it is one of the final steps. Before we can use the data, we need to act on it. We need to clean, […] ...

In this article, we will take a look at how we can use image augmentation in deep learning. Data augmentation is a very useful technique when dealing with image data. Image augmentation is most helpful when the dataset is small. We can also benefit from image augmentation when we are not able to find any […] ...

In this article, we will take a practical approach to some basic image manipulation and transformation using OpenCV and Python. OpenCV is one of the best computer vision libraries and it is easy to use as well. If you want to become a master in the field of computer vision and deep learning, then OpenCV […] ...