Artificial Intelligence: Optimization Algorithms in Python
Learn how to build optimization algorithms from the ground up!
Many data scientists choose to optimize by using pre-built machine learning libraries. But we think that this kind of ‘plug-and-play’ study hinders your learning. That’s why this course gets you to build an optimization algorithm from the ground up. In Artificial Intelligence: Optimization Algorithms in Python, you’ll get to learn all the logic and math behind optimization algorithms. With two highly practical case studies, you’ll also find out how to apply them to solve real-world problems.
In the first case study, we’ll optimize travel plans for six friends who want to fly out from the same airport. In the second case study, we’ll optimize the way university administrators allocate dorm rooms to new students. On the way, we’ll learn what optimization algorithms are. We’ll find out how they can be applied to daily business practice. And we’ll see how they can learn by themselves.
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What you’ll learn
- Learn the theory and implement optimization algorithms from scratch for solving real problems
- Implement step by step the following algorithms in Python: random search, hill climb, simulated annealing, and genetic algorithms
- Solve real problems for optimising flight calendars and dormitory room optimisation (limited resources)
- Implement optimisation algorithms using predefined libraries
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