Name: Certified Reinforcement Learning
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Course Title: Certified Reinforcement Learning
Instructor: Muhammad Shafiq (Data Scientist, AI & ML Engineer, Lecturer, Researcher)
Target Audience:
- Aspiring AI/ML engineers and data scientists
- Professionals seeking reinforcement learning (RL) certification
- Developers interested in game AI, robotics, or autonomous systems
- Intermediate Python programmers with basic machine learning knowledge
Prerequisites:
- Proficiency in Python
- Basic understanding of machine learning and neural networks
- Familiarity with linear algebra and probability (recommended)

Key Topics Covered:
- Fundamentals of Reinforcement Learning (RL):
  - Markov Decision Processes (MDPs) and Bellman equations
  - Agents, environments, rewards, and policies
- Classic RL Methods:
  - Dynamic Programming (Value Iteration, Policy Iteration)
  - Monte Carlo Methods
  - Temporal Difference (TD) Learning (Q-Learning, SARSA)
- Deep Reinforcement Learning (DRL):
  - Deep Q-Networks (DQN) and improvements (Double DQN, Dueling DQN)
  - Policy Gradient Methods (REINFORCE, Actor-Critic)
  - Proximal Policy Optimization (PPO)
  - Model-Based RL and simulation techniques
- Advanced Applications:
  - RL for game-playing agents (e.g., OpenAI Gym, Atari)
  - Robotics control and continuous action spaces
  - Hyperparameter tuning and algorithm debugging
- Certification Preparation:
  - Industry-aligned projects and portfolio development
  - Mock exams and practice tests (3 included)
Key Skills Learned:
- Implementing RL algorithms from scratch in Python
- Building DRL models using TensorFlow and PyTorch
- Designing reward functions and environment simulations
- Training autonomous agents for complex decision-making tasks
- Optimizing RL models for real-world applications
- Preparing for RL certification exams

Duration:
- 3 practice tests (certification readiness)
- Self-paced (lifetime access)
Format:
- On-demand video lectures
- Hands-on coding exercises (Jupyter Notebooks)
- Mobile and TV access
Resources:
- Downloadable Python scripts and Jupyter Notebooks
- Quizzes and assessments for reinforcement
- Project templates for portfolio development