Support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. SVMS is one of the most commonly implemented Machine Learning classification algorithms. In this post I will implement the SMV algorithm from scratch in Python.
Support Vector Machines are perhaps one of the most(if not the most) used classification algorithms. One of the prime advantages of SVM is that it works very good right out of the box. You can take the classifier in it’s generic form, without any explicit modifications, run it directly on your data and get good results.
Decision Tree learning is one of the most widely used and practical methods for inductive inference. Decision Trees are easily understood by human and can be developed/used without much pain. In this post I will walk through the basics and the working of decision trees.
k-means clustering is a method of vector quantization, that can be used for cluster analysis in data mining. K Nearest Neighbours is one of the most commonly implemented Machine Learning clustering algorithms. In this post I will implement the K Means Clustering algorithm from scratch in Python.
OpenMP, short for "Open Multi-Processing", is an API that supports multi-platform shared memory multiprocessing programming in C, C++, and Fortran - on most platforms, processor architectures and operating systems. OpenMP consists of a set of compiler directives, library routines, and environment variables that influence run-time behavior. In this post, we will be exploring OpenMP for C.
One of the prime drawbacks of k-NN is its efficiency. The brute force version of k-NN that was written previously is highly parallelizable. The computation of distances between the attributes is independent of one another. Also, the classification of incoming data points is independent of one another and can be easily accomplished in parallel. In this post I will implement the algorithm from scratch in Python in parallel.
Parallel programming in Python is a bit tricky as compared to languages such as C/C++ and Java. Python is restricted to a single OS thread; therefore, it cannot make use of the multiple cores and processors available on modern hardware. In this post I will use the `multiprocessing` library to easily create and coordinate multiple Python processes and run code in parallel.
k -Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. K Nearest Neighbours is one of the most commonly implemented Machine Learning classification algorithms. In this post I will implement the algorithm from scratch in Python.
Hey there! This is my first post. This post contains some random quotes.