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Cyber Warfare

Cyberwarfare: Evolution & Impact

In the late twentieth century, the widespread use of the Internet revolutionized the speed at which information was collected, computed, and shared. Suddenly, the standard of life became an instantaneous one–where a singular thought could be seen by the rest of the globe in seconds. However, this revolutionary concept did

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Evolution and Future of Digital Learning

Evolution and Future of Digital Learning

In recent years, technology has exponentially advanced beyond automobiles or basic electricity in cities. It has permeated every aspect of our modern world, forcing today’s society—regardless of age, race, nationality, etc.—to adapt accordingly. Younger generations have been raised in a technology-based world, one where children are owning iPads by the

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Deep Learning Basics

Deep learning is a powerful machine learning technique. These are widely used in Natural Language Processing (NLP) image/speech recognition robotics and many other artificial intelligence projects What is Deep Learning? A model which consists of more than three layers in a neural network model is a deep learning model. More

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Building a Deep Learning Model

Building a Deep Learning Model using Keras In this post, let us see how to build a deep learning model using Keras. If you haven’t installed Tensorflow and Keras, I will show the simple way to install these two modules. 1. Installing Tensorflow and Keras Open Anaconda Navigator. Under Environments,

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Confusion Matrix

Confusion Matrix, Accuracy, Precision, Recall, F score explained with an example In this post, we will learn about What is accuracy What are precision, recall, specificity and F score How to manually calculate these measures How to interpret these measures What is confusion matrix and how to construct it What

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Pre-processing

Data Preprocessing – Creating Dummy Variables and Converting Ordinal Variables to Numbers with Examples Data cleaning is a critical step before fitting any statistical model. It includes: Handling missing values Handling outliers Transforming nominal variables to dummy variables (discussed in this post) Converting ordinal data to numbers (discussed in this

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Missing Values

Handling Missing Values in Python In this post, we will discuss: How to check for missing values Different methods to handle missing values Real life data sets often contain missing values. There is no single universally acceptable method to handle missing values. It is often left to the judgement of

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Time Series Components

Components of Time Series In this post, let us explore the four components of time series data. Trend (T) Cyclicality (C) Seasonality (S) Irregular component (I) Let us look at these components one by one. Trend (Secular Trend) Trend is long term movement of the time series. Trend can be

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ARIMA/SARIMA with Python

Autoregressive Integrated Moving Average (ARIMA) is a popular time series forecasting model. It is used in forecasting time series variable such as price, sales, production, demand etc. 1. Basics of ARIMA model As the name suggests, this model involves three parts: Autoregressive part, Integrated and Moving Average part. Let us

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artificial-intelligence

AI/ML In Learning And Development

Cognitive Era Artificial Intelligence in general and Machine Learning in particular (AI/ML) have ushered a new era in many industries, especially in the last two years, albeit with varying degrees of adoption. AI/ML solutions are all around us, and new ones are being created every day, though we may not

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Deep Learning Basics

Deep learning is a powerful machine learning technique. These are widely used in Natural Language Processing (NLP) image/speech recognition robotics and many other artificial intelligence

READ MORE

Confusion Matrix

Confusion Matrix, Accuracy, Precision, Recall, F score explained with an example In this post, we will learn about What is accuracy What are precision, recall,

READ MORE

Pre-processing

Data Preprocessing – Creating Dummy Variables and Converting Ordinal Variables to Numbers with Examples Data cleaning is a critical step before fitting any statistical model.

READ MORE

Missing Values

Handling Missing Values in Python In this post, we will discuss: How to check for missing values Different methods to handle missing values Real life

READ MORE

Time Series Components

Components of Time Series In this post, let us explore the four components of time series data. Trend (T) Cyclicality (C) Seasonality (S) Irregular component

READ MORE

ARIMA/SARIMA with Python

Autoregressive Integrated Moving Average (ARIMA) is a popular time series forecasting model. It is used in forecasting time series variable such as price, sales, production,

READ MORE

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