Building Machine Learning & NLP Models for Cyber Security

Building Machine Learning & NLP Models for Cyber Security
Published 9/2025
Duration: 3h 47m | .MP4 1280×720 30 fps(r) | AAC, 44100 Hz, 2ch | 1.61 GB

Learn how to build intrusion detection model, detect cyber threat, predict vulnerability score, detect phishing email

What you’ll learn
– Learn how to build intrusion detection model using Random Forest Classifier
– Learn how to detect cyber threat using K Nearest Neighbour
– Learn how to predict vulnerability score using MLP Regressor
– Learn how to detect phishing email using NLP and Naive Bayes
– Learn how to build intrusion detection model using Logistic Regression
– Learn how to detect cyber threat using XGBoost
– Learn how to predict vulnerability score using Decision Tree Regressor
– Learn how to analyze user behaviour using unsupervised machine learning and K Means Clustering
– Learn how to conduct exploratory data analysis
– Learn how to perform features selection using Random Forest
– Learn about machine learning and natural language processing applications in cyber security
– Learn how intrusion detection models work. This section covers data collection, preprocessing, feature selection, model training, detecting intrusion
– Learn how to clean dataset by removing missing values and duplicates
– Learn how to test machine learning model in real time simulation
Requirements
– No previous experience in machine learning is required
– Basic knowledge in Python and cyber security

Description
Before getting into the course, we need to ask this question to ourselves, why should we use machine learning to enhance cyber security? Well, here is my answer. From a technical perspective, machine learning can quickly analyze large volumes of security data, detect hidden patterns, and identify potential threats. From a business perspective, it helps businesses reduce risk, prevent costly security breaches, and make faster decisions to protect their digital assets.

Below are things that you can expect to learn from this course:

Learn about machine learning and natural language processing applications in cyber security

Learn how intrusion detection models work. This section covers data collection, data preprocessing, feature selection, splitting data into training and testing sets, model selection, model training, detecting intrusion, model evaluation, deployment, and monitoring

Learn how to clean dataset by removing missing values and duplicates

Learn how to conduct exploratory data analysis

Learn how to perform features selection using Random Forest

Learn how to build intrusion detection model using Random Forest Classifier

Learn how to build intrusion detection model using Logistic Regression

Learn how to detect cyber threat using K Nearest Neighbour

Learn how to detect cyber threat using XGBoost

Learn how to predict vulnerability score using MLP Regressor

Learn how to predict vulnerability score using Decision Tree Regressor

Learn how to detect phishing email using NLP and Naive Bayes

Learn how to analyze user behaviour using unsupervised machine learning and K Means Clustering

Learn how to test machine learning model in real time simulation
Who this course is for:
– Cyber security analysts who are interested in enhancing their system security using machine learning and NLP
– Software engineers who are interested in building intrusion detection model using machine learning
More Info