Overfitting Courses and Certifications

Valerio Velardo - The Sound of AI

Solving Overfitting in Neural Networks

Learn techniques to identify and prevent overfitting in neural networks, including early stopping, audio data augmentation, dropout, and L1/L2 regularization, with practical implementation in a music genre classifier.

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

Richard McElreath

Statistical Rethinking 2022 - Overfitting

Explore overfitting in statistical modeling, covering cross-validation, regularization, information criteria, and robust regression techniques for improved predictive accuracy.

• YouTube
• 1 hour 12 minutes
• Self-Paced
• Free Video

Practical Machine Learning

27 ratings

Learn essential machine learning concepts and techniques, from data collection to model evaluation, with hands-on practice using popular algorithms and the caret package in R.

• Coursera
• 8-9 hours
• Self-Paced
• Paid Course

Institute for Pure & Applied Mathematics (IPAM)

Overfitting: Benign, Tempered and Harmful - Lecture on Machine Learning Regularization

Explore the nuanced understanding of overfitting in neural networks, examining benign, tempered, and harmful effects on model performance based on data properties and training conditions.

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

UofU Data Science

Understanding Overfitting in Machine Learning with Decision Trees - Lecture 5a

Understand how overfitting occurs in machine learning models, with practical examples using decision trees to identify and prevent this common challenge.

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

Association for Computing Machinery (ACM)

Does Learning Require Memorization? A Short Tale About a Long Tail

Explore the relationship between learning and memorization in machine learning, focusing on dataset fitting, interpolation, and long-tailed data distribution challenges.

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

Yannic Kilcher

Grokking - Generalization Beyond Overfitting on Small Algorithmic Datasets

Explore the fascinating "grokking" phenomenon in neural networks, where sudden pattern recognition leads to perfect generalization. Discover its implications for deep learning and algorithmic datasets.

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

Introduction to Deep Learning with PyTorch

43 ratings at DataCamp

Learn how to build your first neural network, adjust hyperparameters, and tackle classification and regression problems in PyTorch.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Winning a Kaggle Competition in Python

Learn how to approach and win competitions on Kaggle.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Google DeepMind: Design And Train Neural Networks

n this Google DeepMind course you will focus on the training process for machine learning models.

• DataCamp
• 4 hours
• Self-Paced
• Free Trial Available

Machine Learning for Absolute Beginners - Level 1

33805 ratings at Udemy

Learn the Fundamental Concepts of Artificial Intelligence and Machine Learning as the Next Game-Changing Technology

• Udemy
• 4 hours 28 minutes
• Self-Paced
• Paid Course

1littlecoder

Overfitting, Underfitting, and Random Forests in Machine Learning - Day 10

Explore overfitting, underfitting, and random forests in machine learning. Learn to build accurate models and apply these concepts to improve your ML skills.

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

NPTEL-NOC IITM

Training a Neural Network: Underfitting and Overfitting

Learn techniques for training neural networks, including strategies to prevent underfitting and overfitting, enhancing model performance and generalization.

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

Simons Institute

Reconsidering Overfitting in the Age of Overparameterized Models

Explore modern machine learning paradigms, focusing on overparameterized models' ability to generalize well despite near-zero training error, challenging classical overfitting concepts.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

Molecular Imaging & Therapy

A Guide to Cross-Validation for AI - Avoiding Overfitting and Ensuring Generalizability

Explore cross-validation techniques for AI, including K-fold and nested methods. Learn to avoid overfitting, ensure generalizability, and select optimal approaches for model evaluation.

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