Course Details

Machine Learning for Cyber Security

With the rapid development in data analytics and sophisticated data science, the addition of machine learning ushers in a new era of data-driven decision making for many industries. Data science is an essential skill for analyzing and deriving useful insights from data, big and small. Machine learning algorithms that iteratively learn from data, enables the computers to find hidden insights without being explicitly programmed. The advanced analytics technology, especially with machine learning, help the organization to predict what will happen in the future based on the analysis of their existing data. The art of machine learning in data science is to create accurate models to guide future actions and to discover patterns that we have never seen before. Over the past decade, machine learning algorithms have learned to detect credit card fraud by mining data on past transactions, learned to steer vehicles driving autonomously on public highways, learned the reading interests of many individuals to assemble personally customized electronic newspaper, and improved personalized medicine. Now-a-days people probably use machine learning dozens of times a day without knowing it.

This Courses offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

Course Content:

• Introduction
  • Machine Learning Basics
  • Data Mining Basics
  • Supervised and Unsupervised Learning
• Regression Analysis
  • Linear Regression
  • Logistic Regression
• Regularization
  • Bias
  • Variance
  • Over-fitting
• Classifiers
  • Decision Trees & Random Forest
  • k-Nearest Neighbor
  • Support Vector Machines
• Clustering
  • K-Means Clustering
• Applying Machine Learning
  • Model Selection
  • Train/Test/Validation sets
  • Learning curves
• Machine Learning Model Design
  • Error Analysis
  • Error Metric
  • Precision and Recall
• Dimensionality Reduction
  • Principal Component Analysis