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The goal of fake news detection is to uncover items that intentionally misinform or deceive readers. Fake news is frequently generated to alter public opinion or for political purposes. After going through this tutorial, you will be able to understand and implement a fake news detection model on Twitter. Let’s get started. What is Fake News? Fake news is defined as “news pieces that are purposely and verifiably untrue”. Who generates fake news and why? Those that run fake news websites want as many people to visit their sites as possible. While some may want their visitors to view the information and be influenced by it, others merely want internet users to click on it, which typically leads to a website where users see more content and advertisements, generating more money for the website owner. The Pipeline of the Project: Fake News Detection using Python I will walk you through step-by-step how we can …

Fake News Detection Project using Python and Machine Learning Read More »

In this article, we will explain what is regression in machine learning. What is Regression? Regression is one type of supervised learning. This technique is used to predict continuous outcomes, unlike classification, where we predict categorical values. In other words, in regression, we investigate the relationship between independent variables (the features) and the dependent variable …

What is Regression in Machine Learning? Complete Guide Read More »

To run and execute Deep learning models, we can use different libraries such as TensorFlow, Torch, Theano, and Keras in python and other languages In this article, we will focus on Tensoflow 2.0 library and its implementation in python. What is TensorFlow? TensorFlow is an open-source library developed by the Google Brain Team for large …

Tensorflow 2.0 in Deep Learning: All the Basics you Need to Know Read More »

Can you believe that those people do not exist? This website by Phillip Wang: thispersondoesnotexist.com shows a new image of a person each time you reload the page, and guess what! none of those people is actually real! Or even more, you can visit the website thisxdoesnotexist.com to see horses, cats, and other objects that …

Friendly Introduction to Generative Adversarial Networks GANs Read More »

Using the right performance metric for the right task Model Performance evaluation is a crucial step of any machine learning workflow. Those evaluation metrics estimate if the model is ready for production or needs some parameters fine-tuning. But how to choose the appropriate metric in your classification use case? In this article, we will explain …

Performance Metrics for Classification in Machine learning Read More »

One of the fundamental methods of machine learning and deep learning is Gradient descent. In this article, we will try to explain this concept and how it works, especially in a neural network architecture. What is gradient descent: Let’s recall some key elements in deep learning first. A deep learning model tries to learn a …

Gradient Descent in Deep Learning Read More »

Reinforcement Learning is a major area of interest within the field of machine learning. Its ability to learn without input data is opening new opportunities in different areas such as robotics and gaming. What is Reinforcement Learning? Reinforcement Learning is a type of Machine Learning technique based on Learning by feedback. Reinforcement Learning is a …

Introduction to Reinforcement learning Read More »

Improving the decision-making of the company is the ultimate goal of data science. It drives decision-making in every part of this modern society By suggesting what advertisement you watch, books you read, and Music you listen to. By recommending friends on social media, and emails that are filtered to spam on your mailbox. This massive …

How Data Science is helping Businesses Read More »

Deep learning works better than traditional machine learning algorithms in many cases. Difference between deep learning and machine learning: Machine learning (ML) is a subfield of AI that consists of imitating human intelligence and learning from an experience without being previously programmed. ML algorithms include traditional methods such as Linear Regression, Logistic Regression, Decision Tree, …

When to Choose Neural Networks over Traditional Machine Learning Read More »