Detect Fake News with Machine Learning & Feature Engineering
Detect Fake News with Machine Learning & Feature Engineering, available at $34.99, has an average rating of 4.5, with 23 lectures, based on 13 reviews, and has 3065 subscribers.
You will learn about 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 This course is ideal for individuals who are People who are interested in building fake news detection system using feature engineering, logistic regression, and machine learning or People who are interested in detecting emotion and and sensationalism in fake news using NLP It is particularly useful for People who are interested in building fake news detection system using feature engineering, logistic regression, and machine learning or People who are interested in detecting emotion and and sensationalism in fake news using NLP.
Enroll now: Detect Fake News with Machine Learning & Feature Engineering
Summary
Title: Detect Fake News with Machine Learning & Feature Engineering
Price: $34.99
Average Rating: 4.5
Number of Lectures: 23
Number of Published Lectures: 23
Number of Curriculum Items: 23
Number of Published Curriculum Objects: 23
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will 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
Who Should Attend
- 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
Target Audiences
- 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
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:
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Learn the basic fundamentals of fake news detection model
-
Case study: applying feature engineering to predict if a news title is real or fake
-
Learn factors that contribute to the widespread of fake news & misinformation
-
Learn how to find and download datasets from Kaggle
-
Learn how to clean dataset by removing missing rows and duplicate values
-
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 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
-
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
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction to the Course
Lecture 2: Table of Contents
Lecture 3: Whom This Course is Intended for?
Chapter 2: Tools, IDE, and Datasets
Lecture 1: Tools, IDE, and Datasets
Chapter 3: Introduction to Fake News Detection System
Lecture 1: Introduction to Fake News Detection System
Chapter 4: Feature Engineering for Detecting Fake News
Lecture 1: Feature Engineering for Detecting Fake News
Chapter 5: Factors That Contribute to the Widespread of Fake News & Misinformation
Lecture 1: Factors That Contribute to the Widespread of Fake News & Misinformation
Chapter 6: Setting Up Google Colab IDE
Lecture 1: Setting Up Google Colab IDE
Chapter 7: Finding & Downloading Fake News Dataset From Kaggle
Lecture 1: Finding & Downloading Fake News Dataset From Kaggle
Chapter 8: Project Preparation
Lecture 1: Uploading Fake News Dataset to Google Colab
Lecture 2: Quick Overview of Fake News Dataset
Chapter 9: Cleaning Fake News Dataset by Removing Missing Values & Duplicates
Lecture 1: Cleaning Fake News Dataset by Removing Missing Values & Duplicates
Chapter 10: News Source Credibility Analysis
Lecture 1: News Source Credibility Analysis
Chapter 11: Detecting Keywords Associated with Fake News
Lecture 1: Detecting Keywords Associated with Fake News
Chapter 12: News Title & Text Length Analysis
Lecture 1: News Title & Text Length Analysis
Chapter 13: Detecting Sensationalism in Fake News
Lecture 1: Detecting Sensationalism in Fake News
Chapter 14: Analyzing Emotion in Fake News with NLP
Lecture 1: Analyzing Emotion in Fake News with NLP
Chapter 15: Detecting Fake News with Feature Engineering
Lecture 1: Detecting Fake News with Feature Engineering
Chapter 16: Detecting Fake News with Logistic Regression
Lecture 1: Detecting Fake News with Logistic Regression
Chapter 17: Detecting Fake News with Random Forest
Lecture 1: Detecting Fake News with Random Forest
Chapter 18: Evaluating Fake News Detection Model with Confusion Matrix
Lecture 1: Evaluating Fake News Detection Model with Confusion Matrix
Chapter 19: Performing Fairness Audit
Lecture 1: Performing Fairness Audit
Chapter 20: Conclusion & Summary
Lecture 1: Conclusion & Summary
Instructors
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Christ Raharja
Ex Technology Risk Consultant, and E-commerce enthusiast
Rating Distribution
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- 2 stars: 0 votes
- 3 stars: 2 votes
- 4 stars: 4 votes
- 5 stars: 7 votes
Frequently Asked Questions
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