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Machine learning with Python fundermentals

3 Units13 Lessons
Unit 1

Supervised Learning

Introduction to Supervised Learning Concepts
Regression Techniques in Supervised Learning
Classification Algorithms for Supervised Models
Real-World Project: Building a Predictive Model for Medical Diagnosis
Unit 2

Unsupervised Learning

Introduction to Unsupervised Learning Principles
Clustering Methods in Unsupervised Learning
Dimensionality Reduction Techniques
Real-World Project: Customer Segmentation Using Unsupervised Methods
Unit 3

Reinforcement Learning

Fundamentals of Reinforcement Learning
Markov Decision Processes in Reinforcement Learning
Q-Learning and Value-Based Methods
Policy Gradient Methods in Reinforcement Learning
Real-World Project: Training an Agent for Autonomous Robot Navigation
Unit 3•Chapter 2

Markov Decision Processes in Reinforcement Learning

Summary

Summarize content in 250 words or less, focus only on main topics, exclude sponsors and unrelated details, avoid introductory statements.

Concept Check

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What does the discount factor γ represent in MDPs?

In MDPs, when does value iteration converge?

What is the purpose of policy evaluation in MDPs?

Which equation defines optimal Q-function in MDPs?

How does exploration affect MDPs in reinforcement learning?

PreviousFundamentals of Reinforcement Learning
NextQ-Learning and Value-Based Methods