A Simple Example using Gaussian Mixture Modeling (GMM) Clustering

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In this article, we will try out unsupervised Machine Learning methods of clustering, for grouping hosts in a IP network in to different clusters based on the similarity in their IP addresses. Importantly, we will see the difference between K-Means and GMM (Gaussian Mixture Model) in their approaches in clustering, and compare their results. We will also see how we can find out the outliers/anomalies in the data using GMM more effectively/

Introduction

Clustering is an unsupervised class of ML algorithms which separate and group given data points in to distinct groups of data in such a way that data points…


A Beginner’s Guide With A Step-by-Step Hands-On Example.

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In this article, I will share an example on how we can deploy a locally trained Machine Learning model in cloud using AWS SageMaker service. By “locally trained” , I mean a ML model which is trained locally in our laptop ( i.e. outside AWS cloud ). I will take you through the various steps starting from training a model, to the deployment of the model in the AWS cloud and invoking the deployment from a local client to get predictions.

Introduction

If we google for the ways to deploy a ML model in AWS, we will find quite a few…


An Exercise In Keras Recurrent Neural Networks And LSTM

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In a previous blog, I had explained an example of Time Series Forecast in Python, using classical time series analysis methods like SARIMA. In this blog, I take up an example of training deep neural networks like RNN / LSTM in Keras, for forecasting Time Series.

Introduction

A Time Series is typically defined as a series of values that one or more variables take over successive time periods. For example, sales volume over a period of successive years, average temperature in a city over months etc. If the series is about only…


An example using classical time series analysis methods (SARIMA)

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In this blog, I explain how a simple univariate time series forecasting can be done in python.

Introduction

A Time Series is typically defined as a series of values that one or more variables take over successive time periods. For example, sales volume over a period of successive years, average temperature in a city over months etc. If the series is about only one variable, it is called Univariate Time Series. If the series lists values of more than one variables over different points of time, it is called Multivariate Time Series…

Rajaram Suryanarayanan

Experienced Networking Software Developer on a Machine Learning Journey https://www.linkedin.com/in/rajaramsurya/

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