> For the complete documentation index, see [llms.txt](https://moharat.gitbook.io/cylabs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://moharat.gitbook.io/cylabs/ai-ml-and-data-science/security-analytics.md).

# Security Analytics

* * practical data science, statistics, probability, and machine learning
  * Data Science, Artificial Intelligence, and Machine Learning
  * 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
  * 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
*  
* * Fundamentals of python
  * * Lists
    * Arrays
    * Tuples
    * Dictionaries
    * NumPy variants
    * Connecting sql
    * Connecting mongo dB
    * Reading and writing csv
    * Web scraping
  * Data Acquisition
  * Data Cleaning
  * Data Manipulation
  * Statistics
  * * Statistics Fundamentals: Medians and Means
    * Statistics Fundamentals: Variance, Deviations, and Robust Measures
    * Applications of Statistics to Data Identification
    * Probability, Bayes, and Phishing
    * Threat Hunting through Signals Analysis
    * Bayesian theorem and inference
    * Linear algebra
    * Signal analysis
    * * Fourier series
      * Fast Fourier Transformation
      * Discreate Fourier Transformation
  * Fundamentals of Machine Learning
  * * Clustering
    * Unsupervised learning
    * Support vector machines
    * Kernel functions
    * Support vector classifiers
    * K-means
    * KNN
    * Elbow Functions and PCA
    * High dimensions
    * Dimensionality reduction
    * Primary component analysis
    * DCBSCAN
    * Decision Trees
    * Random Forests
    * Anomaly detection
    * Supervised learning
    * * Linear regression
      * Deep learning neural networks
      * Multi class classification
      * Predictive models
      * Forecasting and trend analysis for anomaly detection
      * NN and dense networks for phishing detection
      * Network protocol classification
      * Polyfit Regressions
      * Hello, World! Sentiment Analysis
      * Ham vs. Spam via Deep Learning
      * Identifying Protocols
      * Protocol Anomaly Detection
      * Regression and fitting
      * Loss and Error functions
      * Vectors, Matrices, and Tensors
      * Fundamentals of the Perceptron
      * Dense Networks
    * CNN
    * * Predictive identification zero day malware
      * Auto encoders
      * Latent representation
      * Reconstruction loss function work
      * Predictive Malware Identification - Finding Zero Days
      * Ham vs. Spam, CNN Style
      * Multi-class text classification via CNNs
      * Log Anomaly Detection using Autoencoders
      * Real-time Network Anomalies
      * Convolutional Neural Networks
      * Embedding Layers
      * Applying CNNs to text problems
      * Autoencoders
      * Reconstruction loss measurements
      * Creating ensemble autoencoders
      * CNNs and fully connected networks for solving regression problems
      * deep neural network using TensorFlow
      * Solving CAPTCHAs: POC
      * Solving CAPTCHAs: Functional API
      * Solving CAPTCHAs: Split model
      * Genetic Algorithms
      * Convolutional Neural Networks
      * Functional definition of Neural Networks
      * Deep Learning Networks with Multiple Outputs
      * Thinking about Machine Learning Problems
      * Genetic Algorithms
*  
*  
*  
*  
*  
*  
*  
*