Mastering AI, ML and Python in 10 Days

1. Fundamental Concepts

2. Role of AI in Security

3. Types of ML Algorithms in Security

1. Quality Training Data

2. Data Preprocessing and Feature Extraction

3. Model Training Techniques

1. Capabilities of ChatGPT and GPT4all

2. Challenges and Limitations

1. GPT4all Plugins Overview

2. Types of Plugins

3. Integration into AI Security Systems

1. Model Vulnerabilities and Attacks

2. Data Privacy and Compliance

1. Evaluation Metrics

2. Model Validation

3. Real-time Monitoring

1. Cybersecurity Solutions

2. Surveillance Systems

3. Threat Intelligence

1. Emerging Technologies:

2. Ethical Considerations

3. Evolving Threat Landscape

Understanding how to use Python for algebra and statistics is crucial for evaluating the significance of data and determining factors that affect machine learning models.

• Basic algebra for handling matrices and solving equations.

• Descriptive statistics for summarizing data.

• Statistical tests for hypothesis testing and significance analysis.

• Tools for assessing normality and computing confidence intervals.

• Regression analysis for understanding relationships between variables.

Now that we learned about how Python is used for AI, lets try a few exercises to test our knowledge but more importantly get our hands on experience with python and the libraries we will need in production to create AI models. This and the next two additional models are meant to give you hands on experience with Jupyter notebooks and Google Collaborate (you will need to create a google account for this!).

• Learn about Google Collaborate

• Learn about Jupyter Notebooks as a testing playground for code

• Learn about practical steps to create your own model for IP blocking and IPS / IDS.

• Learn how to use other data sources and standardize them

This video teaches you how to create multiple models after standardizing the data, extracting the values then using both Random Forest and Deep Neural Network models. It expands on the model functionality and shows how to evaluate between two models to choose the best fit for a use case.

• Learn about python code used for standardizing data

• Learn how to use python to evaluate models

• Learn how to tweak epoch training using model analysis and charts

• Learn how to let python analyze which model is the best fit for your use case and data

This hands on section goes through ChatGPT, how to use it, how to prompt, and creat and modify settings of how the models respond to your requests and queries.

• Learn how to use ChatGPT in Web and Python code

• Learn how to tweak various types of prompts to get better results

• Learn how to create your own bots in your personal ChatGPT subscription.

• Typical use-cases

This video, from my AI course teaches you how to create your own AI Based Cybersecurity EDR.

In this lesson we are combining both the AI, ML items in the other course ChatGPT, AI and ML for cybersecurity with the skills in this course Threat Hunting and Threat Intelligence.

• We look at the MITRE ATT&CK TTPs

• We create sample data for EDR relevant events like User, Host, Commands, Suspicious Connections

• We create the data standards for various buckets of data and standardize them for AI/ML

• We Link Data with TTPS

• We train two different models

• Assess the two different Models