Implementing Machine Learning Algorithms to classify emails — Email Classification is a Machine Learning problem that falls under the category of Supervised Learning. This mini-project of Email Classification is inspired by J.K. Rowling’s publishing of a book under a pen-name. Udacity’s “Introduction to Machine Learning” provides a comprehensive study of the algorithms and the project. Introduction to Machine Learning Course | Udacity Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources…www.udacity.com

The Reality of Revolutions unfolded by the Animals of Manor Farm — “Animal Farm” is a political satire written by George Orwell in 1946. Written as a short novel of 112 pages, the novella revolves around the story of a farm, where animals began a revolution to free themselves of human control. Through the use of animal character and subtle meanings embedded…

Using the K-Means Algorithm for Vector Quantization of a Raccoon Grayscale Image — Vector Quantization Vector Quantization is a lossy data compression technique. It allows the modeling of the probability density function by the distribution of the prototype vectors. There is some modification of data that renders the compression lossy. Vector Quantization works by dividing a large set of points (vectors) into groups having approximately…

Using Python to implement the various SVC kernels on the Iris Dataset — In this article, we will go through the SVC algorithm in the Sklearn library and experiment with the different kernels on the Iris Dataset. Support Vector Classifier Support Vector Classifier (SVC) is a supervised machine learning model used for two-group classification problems. …

Implementing the DBSCAN Algorithm to find Core Samples — DBSCAN — short for Density-Based Spatial Clustering of Application with Noise, is a density-based clustering algorithm. Clusters are formed based on the density parameters. Density, in terms of DBSCAN, means the number of points that are located in a given area. …

Implementing Machine Learning Classification Algorithms to Recognize Handwritten Digits — Handwritten Digit Recognition is an interesting machine learning problem in which we have to identify the handwritten digits through various classification algorithms. There are a number of ways and algorithms to recognize handwritten digits, including Deep Learning/CNN, SVM, Gaussian Naive Bayes, KNN, Decision Trees, Random Forests, etc.

A step by step guide to using PCA’s Eigenfaces & SVM for Facial Recognition — In this article, we will learn to use Principal Component Analysis and Support Vector Machines for building a facial recognition model. First, let us understand what PCA and SVM are: Principal Component Analysis: Principal Component Analysis (PCA) is a machine learning algorithm that is widely used in exploratory data analysis and for making…

Using Python to dive into the biggest corporate fraud in American History and derive insights — The Enron fraud is a big, messy and totally fascinating story about corporate malfeasance of nearly every imaginable type. In this article, we will use Python to analyze the dataset, and find out patterns and clues through data exploration, as well as build a regression model that could predict the…

The 5 Technical Indicators to Predict the Market — In this article, we will go through the 5 basic (yet powerful) technical indicators to understand the bullish and bearish market trends. We will start by understanding the stock market prediction and then dive into a few indicators in order to understand bullish and bearish trends! Stock market prediction is…