Fahath KG

Deep Learning and Neural Networks Neural network is a kind of ML algorithm which has got developed, inspired by the functions of an actual human brain. Neural networks has the ability to learn from the inputs. With it's ability to learn by itself, Neural networks opens the gate of possibilities to get a solution to most of the complex problems. The significant progress of neural networks in recent days makes it inevitable towards achieving Artificial Intelligence. A neural network uses the examples to automatically infer rules for recognizing the patterns. By increasing the number of training examples, the network can learn more about the patterns and improve the accuracy. Neural networks truly have the potential to revolutionize the field of Artificial Intelligence. Neural networks are used for classification tasks where an object can fall into one of atleast two different categories. A neural network is highly structured and comes in layers. The first layer is the ...

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...

An Introduction to Factor Analysis Hi Guys, in this article I will be giving a theoretical overview about Factor Analysis. This article could also be considered as a continuation of my previous article on Structural Equation Modelling . Since a prior knowledge on Factor analysis is very important to have a better understanding of SEM, I would like to give separate and dedicated article related to Factor Analysis and Path Analysis. This article will cover the Factor Analysis and soon there will be a separate one for Path Analysis too. Lets get started, What exactly a Factor Analysis is? The theory behind the factor analysis is that, the information gained about the inter-dependencies between observed variables can be used later to reduce the set of variables in a dataset. In general, it can be considered as a Data reduction or Dimensionality reduction technique. It's objective is to find out the latent factors that create a commonality among the set of variables. The...

Structural Equation Modelling - A basic introduction. Hi Folks, before I begin, I would like to give a overview about the content in this post. This article gives a theoretical overview about the Structural Equation Modelling. This article is targeted and primarily helpful for beginners. The main reason why I have been intended to write this article is to provide an easy understanding about the concept of SEM in a layman language. I humbly admit that, I'm not going to deal with the entire concepts, principles and explanations related to SEM. However, I hope that this article at least provides you a better idea of the rudiments lying under the hood of SEM. Let me give a quick introduction about myself(Sorry for the interruption). I'm a Software Engineer (Business Intelligence & Analytics). A person with ardent interest and curiosity in dealing with data. Data Science and Machine Learning enthusiast. Data Science and Machine Learning blogger. An R & Python(jus...

Several days ago, I've shared a link as a post in my LinkedIn profile, which gives a better introduction about Data Science. Here, again I would like to share the same link, so that it may helpful to many..... Please follow the link... https://www.edureka.co/blog/what-is-data-science/