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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 4

Policy Gradient Methods in Reinforcement Learning

Summary

User instructs to create a summary of a YouTube transcript that is 250 words or less, focusing solely on the main topic while omitting sponsors and unrelated details, and avoiding any introductory statements

Concept Check

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What is the core idea of the REINFORCE algorithm?

How does the policy gradient theorem update parameters?

What role does the baseline serve in policy gradients?

Why is entropy regularization added in some methods?

What distinguishes on-policy from off-policy gradients?

PreviousQ-Learning and Value-Based Methods
NextReal-World Project: Training an Agent for Autonomous Robot Navigation