Hands-On Genetic Algorithms with Python
Apply genetic algorithms to solve real-world AI and machine learning problems
Created by Eyal Wirsansky
Explore how genetic algorithms can be used to tackle real-world AI and machine learning challenges with Python. Build practical skills to optimize models, solve complex problems, and boost your understanding of bio-inspired computing. Gain hands-on experience applying these techniques across a range of domains.
Packt | Jul 2024 | 418 min
What You Will Learn
You will start by learning the core principles behind genetic algorithms and see how they differ from traditional methods. Through practical Python examples, you will implement algorithms for search, optimization, and AI tasks. Projects and exercises help you apply what you learn to real scenarios, including cloud-based solutions and advanced topics.
Key Features
- Implement genetic algorithms using Python libraries like DEAP and scikit-learn
- Apply optimization strategies to planning, scheduling, and analytics tasks
- Enhance machine learning models and explore explainable AI with genetic approaches
Target Audience
Ideal for data scientists, software developers, and AI enthusiasts with Python experience who want to expand their toolkit. If you are looking to apply genetic algorithms to intelligent applications and optimize machine learning models, you will find practical guidance and actionable techniques here.
