What will you learn in this Reinforcement Learning Specialization Course
Understand the fundamentals of reinforcement learning (RL) and how it applies to real-world problems.
Learn key RL algorithms, including Temporal-Difference learning, Monte Carlo methods, Sarsa, Q-learning, Policy Gradients, and Dyna.
Develop the ability to formalize tasks as RL problems and implement solutions using Python.
Gain insights into how RL complements other machine learning paradigms like supervised and unsupervised learning.
Program Overview
Fundamentals of Reinforcement Learning
⏳ 4 weeks
- Introduction to RL concepts, including Markov Decision Processes (MDPs), value functions, and dynamic programming.
Sample-based Learning Methods
⏳ 4 weeks
- Exploration of learning methods like Monte Carlo and Temporal-Difference learning without explicit environment models.
Prediction and Control with Function Approximation
⏳ 4 weeks
- Application of function approximation techniques, such as neural networks, to handle large state and action spaces.
A Complete Reinforcement Learning System (Capstone)
⏳ 4 weeks
- Integration of concepts learned to build a complete RL solution for a real-world problem.
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Job Outlook
Equips learners with practical skills applicable to roles such as Machine Learning Engineer, AI Specialist, and Data Scientist.
Provides a strong foundation for advanced studies or careers involving autonomous systems, robotics, and intelligent decision-making.
Enhances qualifications for positions requiring expertise in adaptive learning systems and AI.
Specification: Reinforcement Learning Specialization
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