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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 2•Chapter 3

Dimensionality Reduction Techniques

Summary

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Concept Check

0/5

What matrix is diagonalized in PCA for principal components?

Which technique preserves local structures in data?

How does LDA differ from PCA?

What is a non-linear dimensionality reduction method?

In autoencoders, what reduces dimensionality?

PreviousClustering Methods in Unsupervised Learning
NextReal-World Project: Customer Segmentation Using Unsupervised Methods