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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 1

Fundamentals of Reinforcement Learning

Summary

Summarize transcripts to 250 words maximum, exclude sponsors and unrelated content, avoid introductory phrases.

Concept Check

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In reinforcement learning, what is the purpose of the discount factor gamma?

What distinguishes Q-learning from SARSA in RL algorithms?

How does the Markov property apply to states in RL?

What role does the reward function play in MDP?

In policy gradient methods, what is the objective?

NextMarkov Decision Processes in Reinforcement Learning