Binary Classification Courses and Certifications

Train and Evaluate Classification Models

Train and evaluate classification models using Scikit-Learn. Learn binary classification, logistic functions, thresholds, and performance metrics for effective machine learning categorization.

• YouTube
• 1 hour 29 minutes
• On-Demand
• Free Video

Krish Naik

Performance Metrics for Classification Problem in Machine Learning - Part 1

Learn key performance metrics for binary classification in machine learning, including recall and precision, to effectively evaluate model performance and make informed decisions.

• YouTube
• 24 minutes
• On-Demand
• Free Video

Institute for Advanced Study

Learning Probability Distributions - What Can, What Can't Be Done - Shai Ben-David

Explore probability distribution learning, its possibilities and limitations. Shai Ben-David discusses density estimation, sample compression schemes, and challenges in statistical learning theory.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Churchill CompSci Talks

PAC Learning

Explore PAC learning framework in machine learning, covering supervised classification, learnability criteria, and VC dimension. Understand theoretical foundations and practical implications.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Paul Hand

Supervised Machine Learning Review

Comprehensive review of supervised machine learning concepts, focusing on statistical frameworks, loss functions, and the intriguing double-descent phenomenon in neural networks.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Valerio Velardo - The Sound of AI

Implementing a Neural Network for Music Genre Classification

Implement a music genre classifier using TensorFlow and MFCC features. Learn key deep learning concepts like binary/multiclass classification, ReLUs, batching, and overfitting while building the neural network.

• YouTube
• 33 minutes
• On-Demand
• Free Video

Association for Computing Machinery (ACM)

The Cost of Fairness in Binary Classification

Explore the cost of fairness in binary classification, analyzing risk, optimal classifiers, and fairness frontiers with implications for stereotypes and decision-making.

• YouTube
• 22 minutes
• On-Demand
• Conference Talk

Institute for Pure & Applied Mathematics (IPAM)

Fair and Interpretable Decision Rules for Binary Classification

Explore fair and interpretable decision rules for binary classification using Boolean rule sets in DNF, with a focus on maximizing accuracy while maintaining group fairness constraints.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Derek Banas

TensorFlow Binary and Multi-class Classification Tutorial

Learn TensorFlow for binary and multi-class classification, covering data handling, model building, optimization, and evaluation techniques for effective machine learning implementations.

• YouTube
• 2 hours 8 minutes
• On-Demand
• Free Video

Text Classification: AI Techniques and Real-World Applications

Discover AI-powered text classification techniques for spam detection, sentiment analysis, and topic categorization, exploring real-world business applications and best practices.

• YouTube
• 14 minutes
• On-Demand
• Free Video

Data Science Festival

Propensity Measurement Using Positive-Unlabelled Bagging

Explore semi-supervised binary classification with Positive-Unlabelled learning for propensity measurement using PU bagging techniques.

• YouTube
• 26 minutes
• On-Demand
• Free Video

Uncertainty in Artificial Intelligence

Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty - UAI 2023 Oral Session 2

Explore the effectiveness of credal set volume as a measure of epistemic uncertainty in binary and multi-class classification, highlighting implications for machine learning.

• YouTube
• 25 minutes
• On-Demand
• Free Video

Institut des Hautes Etudes Scientifiques (IHES)

Weak Signals: Machine Learning Meets Extreme Value Theory

Explore machine learning's intersection with extreme value theory, focusing on angular measure recovery and its applications in classification and anomaly detection.

• YouTube
• 55 minutes
• On-Demand
• Free Video

NPTEL-NOC IITM

Logistic Regression: Introduction to Binary Classification and Linear Classifiers

Master logistic regression for binary classification, covering linear classifiers, sigmoid functions, parameter estimation, and log-likelihood functions.

• YouTube
• 29 minutes
• On-Demand
• Free Video

NPTEL-NOC IITM

Introduction to Logistic Regression

Learn about binary classification, logistic regression, and related concepts like probit and logit links in this concise introduction to fundamental statistical modeling techniques.

• YouTube
• 14 minutes
• On-Demand
• Free Video