Fahath KG

Machine Learning - a basic introduction with step by step implementation Hi Guys, in this article, the rudiments in understanding the Machine Learning and the steps to implement it, has been discussed. I hope this would give you a better understanding of Machine Learning concepts. Let's get started, and begin with the very basic question, What is Machine Learning? Machine Learning(ML) is a sub-field of Computer Science and AI, and contributes to building systems that can learn from data without explicit programming. It has a strong predictive power, as the machine is fit and trained to find patterns in the data. Machine learning is a concept of teaching the machine with the huge volume of dataset and verify the accuracy with the new dataset. In Machine Learning, data is gathered to train a machine learning model, so it can understand patterns within the data. Once the model has been trained, it can be used it to predict the results of out-of-sample data, or d...

PCA - Principal Components & Variables In this post, I've discussed about the relationship between Principal Components (PCs) and Variables in PCA. Most of us would've found it very difficult to figure out the corresponding variables to the PCs in PCA. Let me explain it clearly in the beginning itself that, a PC in a PCA will never corresponds to a particular Variable. A PC is a combination of 2 or more variables. Then how exactly are we going to relate a PC with variable? The rest of the article might give the answer for this question. Ok, lets begin with a quick introduction to PCA. Principal Component Analysis (PCA) is a Dimensionality Reduction technique which is used to reduce the dimension of the data with minimum loss of information. With a lot of open source tools available in the market, it is very easy to implement PCA. For example, let us take R. In R after running PCA the following statistics will be obtained. Standard Deviation, Rotat...