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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 1•Chapter 2

Regression Techniques in Supervised Learning

Summary

Instructions specify to create a summary of no more than 250 words, focusing solely on the core content, excluding any references to sponsors or extraneous details, and avoiding any introductory statements about the summary itself.

Concept Check

0/5

What is the main purpose of L1 regularization in regression?

How does Ridge regression address multicollinearity?

What assumption is key for linear regression validity?

In what scenario is polynomial regression preferred?

What does overfitting mean in regression models?

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