CourseGenix

Explore

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 3

Classification Algorithms for Supervised Models

Summary

User requests transcript summary limited to 250 words, focusing solely on main topic, excluding sponsors and unrelated details, and avoiding introductory statements.

Concept Check

0/5

Which classification algorithm assumes features are conditionally independent?

What technique does Random Forest use to reduce overfitting?

In KNN, what is the effect of the curse of dimensionality?

What is the primary advantage of using kernel SVM?

Which algorithm uses the hinge loss function?

PreviousRegression Techniques in Supervised Learning
NextReal-World Project: Building a Predictive Model for Medical Diagnosis