Confusion Matrix Courses and Certifications

Mastering Confusion Matrix in Machine Learning - From Confusion to Clarity

Master the confusion matrix to evaluate and enhance machine learning model performance through accuracy, precision, and recall metrics for better classification results.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

deeplizard

CNN Confusion Matrix with PyTorch - Neural Network Programming

Learn to build, plot, and interpret a confusion matrix using PyTorch for neural network evaluation, including techniques for disabling gradient tracking and analyzing model performance across different classes.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

NPTEL-NOC IITM

Statistical Inference of Logistic Regression: Confusion Matrix, Errors, and Model Selection

Explore statistical inference in logistic regression, covering confusion matrices, error types, and model selection techniques for improved predictive accuracy.

• YouTube
• 17 minutes
• Self-Paced
• Free Video

Roboflow

Model Evaluation for Computer Vision - Precision, Recall, F1 Score, and Confusion Matrix

Explore key metrics for evaluating computer vision models, including precision, recall, F1 score, and confusion matrices, to enhance your understanding of model performance.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

Classification Algorithms and Metrics

Master classification algorithms by building Logistic Regression, k-NN, Naive Bayes, and Decision Trees from scratch. Implement metrics like AUCROC and confusion matrix while gaining hands-on experience with real-world applications.

• CodeSignal
• 5 hours
• Self-Paced
• Free Certificate

Deep Dive into Regression and Classification Metrics

Master essential machine learning metrics for evaluating regression and classification models, from basic error measurements to advanced ROC curves and confusion matrices.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Building and Evaluating Text Classifiers in Python

Master text classification techniques using Naive Bayes, logistic regression, and grid search optimization. Learn preprocessing, model training, cross-validation, and performance evaluation using Python and the SMS Spam dataset.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Classification Algorithms and Metrics

Dive into classification algorithms by building Logistic Regression, k-NN, Naive Bayes, and Decision Trees from scratch without libraries, plus implement AUCROC metrics.

• CodeSignal
• 5 hours
• Self-Paced
• Free Certificate

Evaluation Metrics & Advanced Techniques for Imbalanced Data

Master evaluation metrics and advanced techniques for handling imbalanced datasets, including cost-sensitive learning, ensemble methods, and anomaly detection strategies.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Coursera

Support Vector Machines in Python, From Start to Finish

162 ratings at Coursera

Build a Support Vector Machine classifier using scikit-learn and RBF Kernel to predict heart disease. Hands-on project using UCI Machine Learning Repository data, focusing on implementation and evaluation techniques.

• Coursera
• 1 hour 55 minutes
• Self-Paced
• Paid Course

EDUCBA

Python: Logistic Regression & Supervised ML

17 ratings at Coursera

Master logistic regression and supervised ML using Python with the Titanic dataset, covering data analysis, feature engineering, model building, and evaluation techniques.

• Coursera
• 4 hours 39 minutes
• Self-Paced
• Paid Course

Coursera

Evaluate AI Risks: Adopt Smart Predictions

Discover how to evaluate AI prediction models beyond simple accuracy using precision, recall, and confusion matrices to make data-driven adoption decisions.

• Coursera
• 2 hours 9 minutes
• Self-Paced
• Paid Course

EDUCBA

Logistic Regression with R: Build & Predict

Master logistic regression in R through hands-on projects in healthcare and finance, from data preprocessing to model optimization using ROC curves and confusion matrices.

• Coursera
• 8 hours 16 minutes
• Self-Paced
• Paid Course

Coursera

Train ML Models

Master supervised ML for land-cover classification using Random Forest, feature engineering, and confusion-matrix validation to produce accurate, job-ready geospatial maps.

• Coursera
• 2 hours 14 minutes
• Self-Paced
• Paid Course

EDUCBA

Master Decision Trees in R: Build, Predict & Evaluate

Discover how to build, interpret, and evaluate decision tree models in R for classification and regression using real-world datasets like bank loans and diabetes outcomes.

• Coursera
• 8 hours 8 minutes
• Self-Paced
• Paid Course