Fake News Detection using Machine Learning
What you learn in Fake News Detection using Machine Learning ?
About this Course
There are a lot of news articles where the news is false or cooked up. With so many advances in Natural Language Processing and Machine Learning, we can actually build a Machine Learning model on how to detect fake news. We can check if a piece of the news article is genuine or fake. In this fake news detection tutorial, we will be detecting fake news with Machine Learning methods to verify the genuineness of the article. In this course, we shall first start off with an introduction to machine learning. Then, we shall learn about algorithms, such as logistic regression and decision trees. Finally, there will be a demo on python for fake news detection.
Some best universities in the world, like the UT Austin and SRM Institute of Science and Technology, have designed several post-graduate Machine Learning Courses and degree programs by collaborating with Great Learning. These programs are the best in the industry, ranking #1 in India. The curriculum of the online artificial intelligence course has been prepared by the highest-rated faculty to provide learners with advanced AIML skills and techniques. In addition, various industry professionals from top-notch organizations offer learners personalized mentorship, giving them guidance to become successful in their careers.
Learn more about our AIML Online Courses and join today.
Course Outline
Data is the soul of Machine Learning, and there are specific methods to deal with it efficiently. This module first introduces Machine Learning and talks about the mathematical procedures involved. You will learn about supervised and unsupervised learning, Data Science Machine Learning steps, linear regression, Pearson's coefficient, best fit line, and coefficient of determinant. Lastly, you will be going through a case study to help you effectively comprehend Machine Learning concepts.
This section explains logistic regression, its advantages and disadvantages, concordance, and difference between logit and logistic regression. Later you will understand the term error, collinearity, predictive modelling, and the importance of logistic regression. You will also understand to evaluate a logistic regression model.
This section discusses a well-known method of supervised learning which is the Decision Tree. You will also understand the advantages of using a decision tree. Later on, the tutor will cover some important questions based on regression in decision trees, random forest algorithms, gradient-boosted decision trees, unbalanced data, and tools used to draw decision trees.
Our course instructor
Mr. Bharani Akella
Data Scientist
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1.0 Hours
of self-paced video lectures
