Artificial intelligence is no longer a technology exclusive to big companies. In this course you'll learn to apply AI and Machine Learning algorithms with Python to solve real business problems, optimizing processes, reducing operating costs, and boosting revenue through practical cases inspired by real business scenarios.

Throughout the course you'll develop three complete projects based on Reinforcement Learning techniques. You'll implement Q-Learning to optimize robot movement in an e-commerce warehouse, Deep Q-Learning to minimize energy consumption in a data center, and Thompson Sampling to maximize revenue for an e-commerce business. Each project will let you understand both the theory and its practical implementation in Python.

Beyond the practical content, you'll build a solid conceptual foundation in artificial intelligence applied to business. You'll understand how the main algorithms work, when to use them, and how to bring these solutions to real-world scenarios of optimization, automation, and data-driven decision-making. All of it backed by a 100+ page book with theory, mathematics, solved exercises, code templates, and additional resources to reinforce your learning.

If you want to specialize in one of the areas with the highest professional demand, improve your profile as a data scientist, AI engineer or Machine Learning developer, or learn to use artificial intelligence to create more efficient and profitable companies, this course will give you the tools you need to take the next step in your career.