Fundamentals of Machine Learning

Description

• Course ID:
  • IC-805-E-02
• Content:
  • Introduction to Machine Learning (ML)
  • Supervised, Unsupervised, and Reinforcement Learning
  • Data Preparation and Preprocessing
  • Models and Algorithms in ML
  • Evaluation and Validation of ML Models
  • Applications and Practical Examples in ML
• Prerequisites:
  • Basic knowledge in programming and statistics recommended.
• Certification Requirement:
  • Without certification option
• Conduction Method:
  • Online (For on-site or in-house training, please send us a request via our contact form (Contact - intellcert). We will get in touch with you shortly thereafter.)
• Language:
  • English
• Target  Audience:
  • Data analysts
  • Beginners in machine learning
• Learning Objectives:
  • Understanding the basic principles of machine learning (ML), including supervised and unsupervised learning.
  • Knowledge of key algorithms and models such as decision trees, linear regression, and KNN (K-Nearest Neighbors).
  • Ability to analyze training datasets and train models.
  • Introduction to evaluation methods and performance metrics of ML models.
• Note:
  • Displayed price excl. VAT