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Artificial Intelligence & Machine Learning Essential

  • Category: Cyber Security
  • Exam Code: AMLE
  • Type of Question: Multiple-choice question
  • Exam Duration: 120 Minutes
  • Passing Score: 60%
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Description

Artificial Intelligence & Machine Learning Essentials is a foundational course tailored for both beginners and professionals interested in mastering AI and ML concepts. This course covers fundamental principles such as supervised and unsupervised learning, neural networks, natural language processing, and reinforcement learning. Through practical examples and hands-on projects, participants gain proficiency in implementing AI and ML algorithms, preparing them to leverage these technologies across various domains, from healthcare and finance to autonomous systems and recommendation engines.

Course Curriculum

1. Introduction to AI and ML
  1. Understanding AI and ML
    • Definition and Scope of Artificial Intelligence (AI) and Machine Learning (ML)
    • Historical developments and real-world application
    • Distinction between AI and ML
  2. Basic Concepts in Machine Learning
    • Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
    • Feature engineering and data preprocessing
    • Model training, evaluation, and prediction
  3. Introduction to Neural Networks
    • Basics of neural networks
    • Layers, neurons, and activation functions
    • Training neural networks with backpropagation
  1. Data Collection and Cleaning
    • Gathering and cleaning data for ML projects
    • Handling missing values and outliers
    • Data quality and validation
  2. Exploratory Data Analysis (EDA)
    • Visualizing data distributions and patterns
    • Correlation analysis
    • Feature selection and dimensionality reduction
  3. Project: Data Preparation and EDA
    • Applying Data Preparation and EDA techniques to a real-world dataset
  1. Regression
    • Understanding Regression Analysis
    • Linear and non-linear regression models
    • Evaluation metrics for regression
  2. Classification
    • Basics of classification problems
    • Popular Classification Algorithms (e.g., Decision Trees, SVM)
    • Model evaluation and metrics for classification
  3. Project: Building a Supervised Learning Model
    • Implementing a supervised learning model on a provided dataset
  1. Clustering
    • Introduction to Unsupervised Learning
    • Types of Clustering Algorithms (e.g., K-Means, Hierarchical)
    • Applications of Clustering
  2. Dimensionality Reduction
    • Principal Component Analysis (PCA)
    • t-Distributed Stochastic Neighbor Embedding (t-SNE)
    • Use cases for dimensionality reduction
  3. Project: Unsupervised Learning Project
    • Applying clustering and dimensionality reduction to a real-world dataset
  1. Introduction to Deep Learning
    • Deep neural networks and architectures
    • Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)
    • Transfer learning
  2. Practical Applications of AI and ML
    • Use cases and applications in various industries
    • Ethical considerations and responsible AI
    • Future Trends in AI and ML
  3. Capstone Project: Real-world AI and ML Application
    • Developing a comprehensive AI or ML project, integrating various concepts learned throughout the course