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
Material Includes
- Practice Labs
- Paid Tools
- Hack the Box/Try Hack me
- Paid Tools
- Videos
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