Detect Fake News with Machine Learning & Feature Engineering

Learn how to build fake news detection model using machine learning, feature engineering, logistic regression, and NLP
What you'll learn
Learn how to build fake news detection model with feature engineering
Learn how to build fake news detection model with logistic regression
Learn how to build fake news detection model with Random Forest
Case study: applying feature engineering to predict if a news title is real or fake
Learn the basic fundamentals of fake news detection model
Learn factors that contribute to the widespread of fake news & misinformation
Learn how to perform news source credibility
Learn how to detect keywords associated with fake news
Learn how to perform news title and length analysis
Learn how to detect sensationalism in fake news
Learn how to detect emotion in fake new with NLP
Learn how to evaluate fake news detection model with confusion matrix
Learn how to perform fairness audit with demographic parity difference
Learn how to mitigate potential bias in fake news detection
Learn how to clean dataset by removing missing rows and duplicate values
Learn how to find and download datasets from Kaggle
Requirements
No previous experience in machine learning is required
Basic knowledge in Python and statistics
Description
Welcome to Detecting Fake News with Machine Learning course. This is a comprehensive project based course where you will learn step by step on how to build a fake news detection system using feature engineering, logistic regression, and other models. This course is a perfect combination between Python and machine learning, making it an ideal opportunity to enhance your data science skills. The course will be mainly focusing on three major aspects, the first one is data analysis where you will explore the fake news dataset from multiple angles, the second one is predictive modeling where you will learn how to build fake news detection system using big data, and the third one is to mitigate potential biases from the fake news detection models. In the introduction session, you will learn the basic fundamentals of fake news detection models, such as getting to know ethical considerations and common challenges. Then, in the next session, we are going to have a case study where you will learn how to implement feature engineering on a simple dataset to predict if a news is real or fake. In the case study you will specifically learn how to identify the presence of specific words which are frequently used in fake news and calculate the probability of a news article is fake based on the track record of the news publisher. Afterward, you will also learn about several factors that contribute to the widespread of fake news & misinformation, for examples like confirmation bias, social media echo chamber, and clickbait incentives. Once you have learnt all necessary knowledge about the fake news detection model, we will begin the project. Firstly you will be guided step by step on how to set up Google Colab IDE. In addition to that, you will also learn how to find and download fake news dataset from Kaggle, Once, everything is ready, we will enter the main section of the course which is the project section The project will be consisted of three main parts, the first part is the data analysis and visualization where you will explore the dataset from various angles, in the second part, you will learn step by step on how to build a fake news detection system using logistic regression and feature engineering, meanwhile, in the third part, you will learn how to evaluate the model's accuracy. Lastly, at the end of the course, you will learn how to mitigate potential bias in fake news detection systems by diversifying training data and conducting fairness audits.First of all, before getting into the course, we need to ask ourselves this question: why should we build fake news detection systems? Well, here is my answer. In the past couple of years, we have witnessed a significant increase in the number of people using social media and, consequently, an exponential growth in the volume of news and information shared online. While this presents incredible opportunities for communication, however, this surge in information sharing has come at a cost, the rapid spread of unverified, misleading, or completely fabricated news stories. These stories can sway public opinion, incite fear, and even have political and social consequences. In a world where information is power, the ability to distinguish between accurate reporting and deceptive content is very valuable. Last but not least, knowing how to build a complex machine learning model can potentially open a lot of opportunities.Below are things that you can expect to learn from this course:Learn the basic fundamentals of fake news detection modelCase study: applying feature engineering to predict if a news title is real or fakeLearn factors that contribute to the widespread of fake news & misinformationLearn how to find and download datasets from KaggleLearn how to clean dataset by removing missing rows and duplicate valuesLearn how to perform news source credibilityLearn how to detect keywords associated with fake newsLearn how to perform news title and length analysisLearn how to detect sensationalism in fake newsLearn how to detect emotion in fake new with NLPLearn how to build fake news detection model with feature engineeringLearn how to build fake news detection model with logistic regressionLearn how to build fake news detection model with Random ForestLearn how to evaluate fake news detection model with confusion matrixLearn how to perform fairness audit with demographic parity differenceLearn how to mitigate potential bias in fake news detection
Who this course is for
People who are interested in building fake news detection system using feature engineering, logistic regression, and machine learning
People who are interested in detecting emotion and and sensationalism in fake news using NLP
Homepage

https://www.udemy.com/course/detect-fake-news-with-machine-learning-feature-engineering/