Regularization Courses and Certifications

Deep Learning Crash Course for Beginners

1 review

Comprehensive introduction to deep learning fundamentals, covering neural networks, learning types, architectures, and model development steps. No coding required, ideal for beginners.

• freeCodeCamp
• 1-2 hours
• On-Demand
• Free Online Course

Deep Learning with PyTorch: Zero to GANs

1 review

"Deep Learning with PyTorch: Zero to GANs" is a beginner-friendly online course offering a practical and coding-focused introduction to deep learning using the PyTorch framework. Enroll now to start learning.

• Jovian
• 24 weeks, 2-3 hours a week
• On-Demand
• Free Online Course

Machine Learning

1 review

Comprehensive introduction to machine learning concepts, from fundamentals to advanced techniques, with clear explanations and practical applications.

• YouTube
• 22 hours
• On-Demand
• Free Online Course

University of Tübingen

Introduction to Machine Learning

Explore key machine learning concepts and techniques, from linear regression to deep learning, clustering, and dimensionality reduction, with hands-on applications.

• YouTube
• 11 hours
• On-Demand
• Free Online Course

An Introduction to Machine Learning in Quantitative Finance

Discover how machine learning can be used to solve financial data problems and create informative insights and predictions.

• FutureLearn
• 4 weeks, 3 hours a week
• On-Demand
• Paid Course

Training Neural Networks - Part II - Lecture 11

Explore advanced neural network training techniques, including loss functions, learning rates, momentum, and strategies to prevent overfitting for improved computer vision model performance.

• YouTube
• 1 hour 13 minutes
• On-Demand
• Free Online Course

Machine Learning and AI Foundations: Recommendations

325 ratings at LinkedIn Learning

This project-based course shows programmers of all skill levels how to use machine learning to build programs that can make recommendations—like recommending new products.

• LinkedIn Learning
• 58 minutes
• On-Demand
• Free Trial Available

IIT Kharagpur;  NPTEL

Mathematics for Machine Learning

Explore advanced mathematical concepts essential for developing robust machine learning algorithms, including optimization, regularization, and statistical modeling techniques.

• Swayam
• 12 weeks
• On-Demand
• Free Online Course

Deep Learning: Model Optimization and Tuning

208 ratings at LinkedIn Learning

Learn about various optimization and tuning options available for deep learning models and use them to improve models.

• LinkedIn Learning
• 54 minutes
• On-Demand
• Free Trial Available

Deep Learning with PyTorch

12 ratings at DataCamp

Learn to create deep learning models with the PyTorch library.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

MITCBMM

Statistical Learning Theory and Applications - Class 7

Explore statistical learning theory, covering least squares, gradient descent, regularization, and induction, with insights into historical context and practical applications.

• YouTube
• 1 hour 27 minutes
• On-Demand
• Free Online Course

MITCBMM

The Learning Problem and Regularization

Explore the foundations of machine learning with Prof. Poggio, covering key concepts like generalization, consistency, and regularization in solving learning problems.

• YouTube
• 1 hour 19 minutes
• On-Demand
• Free Online Course

MITCBMM

Introduction to Optimization

Learn optimization techniques for machine learning, including likelihood estimation, gradient descent, and regularization, with practical examples and applications.

• YouTube
• 1 hour 12 minutes
• On-Demand
• Free Online Course

Institute for Advanced Study

Tradeoffs Between Robustness and Accuracy - Percy Liang

Explore tradeoffs between robustness and accuracy in machine learning, covering topics like spurious correlations, regularization, and model complexity with Percy Liang.

• YouTube
• 1 hour 3 minutes
• On-Demand
• Free Online Course

Alexander Amini;  MIT

MIT: Introduction to Deep Learning

Foundations of deep learning: perceptrons, neural networks, loss functions, backpropagation, optimization techniques, and strategies to prevent overfitting in neural network training.

• YouTube
• 45 minutes
• On-Demand
• Free Online Course