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Course Overview

This course provides a solid foundation in Genetic Algorithms (GA), focusing on both theory and practical implementation. Learners begin with the core principles and representation techniques, followed by selection methods and genetic operators such as crossover and mutation. The program emphasizes designing effective fitness functions and implementing GA solutions in Python. Advanced modules introduce hybrid GA approaches that combine traditional optimization with evolutionary strategies. Case studies and a final project enable learners to apply GA for solving real-world optimization problems across diverse domains.

Course Content

  • Introduction to Genetic Algorithms
  • Basic Concepts
  • Representation Techniques
  • Selection Methods
  • Genetic Operators – Crossover
  • Genetic Operators – Mutation
  • Fitness Function Design
  • GA Implementation in Python
  • Hybrid GA approaches
  • Case Studies and Project

1: Do I need any experience?

Absolutely! Many AI courses are designed specifically for beginners. They start with foundational concepts and often use beginner-friendly tools and platforms that minimize complex coding. Look for courses labeled "Introductory," "No-code AI," or "AI for Everyone." These will help you build a solid understanding of the principles before you advance to more technical, programming-heavy topics.
These terms are often used interchangeably, but they represent a hierarchy of concepts:
- Artificial Intelligence (AI) is the broadest field, focused on creating machines capable of intelligent behavior.
- Machine Learning (ML) is a subset of AI that gives computers the ability to learn from data without being explicitly programmed for every task.
- Deep Learning (DL) is a further subset of ML that uses complex neural networks to solve advanced problems like image and speech recognition.
Most foundational courses will cover the relationship between these fields, while specialized courses will dive deep into one area.
Our courses are project-based to ensure you gain hands-on experience. You'll work on real-world projects such as building a movie recommendation system, developing a chatbot, or creating an image classifier. These projects form a practical portfolio that demonstrates your skills to employers. This hands-on approach prepares you for roles like AI Specialist, Machine Learning Engineer, or Data Analyst, and equips you with in-demand skills to solve business problems.

Student Reviews

Considering primary motivation for the generation of narratives is a useful concept