Description
Data Science, Artificial Intelligence, and Machine Learning are no longer just buzzwords—they’re becoming essential tools in our information security toolkit. The challenge is, without a background in mathematics or data science, many are left relying on vendors. This course breaks down machine learning and data science, making them accessible to everyone. More than 70% of class time is devoted to hands-on problem-solving, rather than theory alone.
Unlike other courses, this one focus specifically on addressing information security challenges. While many courses lean heavily towards either abstract theory or overly simplistic problems, this course strikes a balance. We cover only the essential theory and math fundamentals, and only as they relate to practical techniques you’ll use. Step by step, you’ll learn to apply statistical, probabilistic, and mathematical tools in real-world scenarios, equipping you with the skills to develop your own machine learning solutions. The hands-on projects are designed to give you a solid foundation for further exploration and implementation of machine learning in security.
Major topics covered include:
- Data acquisition from SQL, NoSQL document stores, web scraping, and other common sources
- Data exploration and visualization
- Descriptive statistics
- Inferential statistics and probability
- Bayesian inference
- Unsupervised learning and clustering
- Deep learning neural networks
- Autoencoders
- Loss functions
- Convolutional networks
- Embedding layers
BUSINESS TAKEAWAYS:
Thise course will help your organization:
- Generate useful visualization dashboards
- Solve problems with Neural networks
- Improve the effectiveness, efficiency, and success of cybersecurity initiatives
- Build custom machine learning solutions for your organization’s specific needs
You Will Be Able To:
- Apply statistical models to real world problems in meaningful ways
- Generate visualizations of your data
- Perform mathematics-based threat hunting on your network
- Understand and apply unsupervised learning/clustering methods
- Build Deep Learning Neural Networks
- Build and understand Convolutional Neural Networks
- Understand and build Genetic Search Algorithms
You Will Receive with This Course:
- A supporting virtual machine
- Jupyter notebooks of all of the labs and complete solutions
This Course Will Prepare You To:
- Build AI anomaly detection tools
- Model information security problems in useful ways
- Build useful visualization dashboards
- Solve problems with Neural networks
Additional Resources:
- Anaconda
- TensorFlow (and supporting libraries)
- Matplotlib
- VMWare Workstation/Player/Fusion
eBooks 2023 download
Download Part1
Download Part2
Download Part3
- Design
