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Artificial Intelligence

Offered By:
Encryptic Security

About This Course:

The Advanced Diploma in Cyber Security by Encryptic Security is a comprehensive program designed to provide participants with advanced skills and expertise in the rapidly evolving field of cybersecurity. This six-month course encompasses six levels, each focusing on specific aspects of cybersecurity, ensuring a well-rounded education.

Module

7 Module 6 months
  • Understanding AI concepts and applications
  • History and evolution of AI
  • Ethical considerations in AI
  • Introduction to basic programming principles (Python)
  • Setting up the development environment
  • Mathematics essentials for AI (Algebra, Statistics, Basics of Calculus)
  • Fundamentals of supervised learning (Regression, Classification)
  • Different types of unsupervised learning (Clustering, Dimensionality Reduction)
  • Model evaluation metrics (Accuracy, Precision, Recall)
  • Cross-validation and hyperparameter tuning
  • Feature engineering techniques
  • Introduction to ensemble methods (Random Forest, Gradient Boosting)
  • Neural networks: architecture and layers
  • Activation functions and backpropagation
  • Understanding Convolutional Neural Networks (CNNs) for image data
  • Recurrent Neural Networks (RNNs) for sequential data
  • Transfer learning and fine-tuning pre-trained models
  • Introduction to Generative Adversarial Networks (GANs)
  • Hands-on projects for practical understanding
  • Introduction to Natural Language Processing (NLP) concepts
  • Text processing techniques (Tokenization, Lemmatization)
  • Sentiment analysis and Named Entity Recognition (NER)
  • Word embeddings (Word2Vec, GloVe)
  • Advanced NLP techniques (BERT, GPT)
  • Introduction to reinforcement learning concepts
  • Deep reinforcement learning
  • Deep dive into Reinforcement Learning algorithms
  • Markov Decision Processes (MDPs) and Q-learning
  • Implementation of Deep Q Networks (DQNs)
  • Policy gradients and actor-critic models
  • Real-world applications of reinforcement learning
  • Reinforcement learning projects and simulations
  • Advanced optimization techniques (Genetic Algorithms, Particle Swarm Optimization)
  • Explainable AI and interpretable models
  • Time series analysis and forecasting using AI
  • Handling imbalanced data and rare events
  • Introduction to domain-specific AI applications (e.g., healthcare, finance)
  • Advanced AI architectures (Capsule Networks, Transformers)
  • Exploring industry-specific AI applications in-depth
  • Challenges and considerations in deploying AI solutions
  • Collaborative capstone project with real-world datasets
  • Final presentation, project showcase, and peer review
  • Post-project reflections and lessons learned
  • Preparing for continuous learning and staying updated on AI advancements
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