Statistical Learning Theory Courses and Certifications

Alexander Amini;  MIT

Introduction to Deep Learning

1 rating

Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.

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

Alberta Machine Intelligence Institute

Data for Machine Learning

98 ratings at Coursera

Develop skills to prepare, engineer, and validate data for machine learning models. Learn to identify biases, improve generality, and enhance model accuracy through thoughtful feature engineering.

• Coursera
• 11 hours 53 minutes
• Self-Paced
• Paid Course

Deep Learning with PyTorch

96 ratings at Coursera

Advance your deep learning skills with PyTorch, covering neural networks, convolutional architectures, and advanced techniques through hands-on exercises and a final CNN project.

• Coursera
• 18 hours 33 minutes
• Self-Paced
• Paid Course

PyTorch: Fundamentals

90 ratings at Coursera

Master PyTorch fundamentals through hands-on coding, building neural networks from single neurons to CNNs, managing datasets, and implementing complete ML workflows with confidence.

• Coursera
• 1 day 10 minutes
• Self-Paced
• Paid Course

Bayesian Statistics: Mixture Models

63 ratings at Coursera

Explore mixture models in Bayesian statistics, covering concepts, estimation methods, and practical applications. Gain hands-on experience with R software for real-world data analysis.

• Coursera
• 21 hours 32 minutes
• Self-Paced
• Paid Course

Learning Deep Learning: Unit 1

1 rating

Discover neural networks, backpropagation, and modern frameworks like TensorFlow through hands-on programming exercises covering deep learning fundamentals and advanced techniques.

• Coursera
• 5 hours 15 minutes
• Self-Paced
• Paid Course

Packt

Foundations and Core Concepts of PyTorch

54 ratings at Coursera

Master PyTorch fundamentals: from setup to advanced modeling. Build neural networks, optimize performance, and apply deep learning techniques to real-world problems using hands-on exercises and practical implementations.

• Coursera
• 6 hours 49 minutes
• Self-Paced
• Paid Course

Career Certificate

Introduction to Data Science Techniques

22 ratings at Coursera

Gain insights into essential data science techniques, from time series analysis and statistical learning to data preparation, developing skills to build predictive models and interpret statistical outputs.

• Coursera
• 24 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 3 courses

Neural Networks and Random Forests

18 ratings at Coursera

Explore advanced AI techniques: neural networks and random forests. Learn structure, coding, and applications. Complete projects on heart disease prediction and patient similarity analysis.

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

Advanced Bayesian Methods and Applications

Master advanced Bayesian inference with variational inference, decision theory, and non-parametric methods. Use PyMC3 for probabilistic programming and apply models to sports analytics and medicine.

• Coursera
• 6 weeks, 4 hours a week
• Self-Paced
• Paid Course

Mathematical Thinking for Advanced Mathematics

Explore mathematical thinking processes—generalization, unification, visual reasoning, and axiomatic systems—to excel in advanced math courses and research in engineering, computer science, and beyond.

• Coursera
• 8 weeks, 3 hours a week
• Self-Paced
• Paid Course

Statistical Learning

Explore cutting-edge statistical analysis techniques, from regression to advanced methods like kernel smoothing and support vector machines. Enhance data interpretation skills with hands-on Python examples.

• Coursera
• 4 days 18 hours 15 minutes
• Self-Paced
• Paid Course

Machine Learning for Engineers: Algorithms and Applications

Master practical machine learning algorithms through hands-on implementation in Python and PyTorch, covering supervised and unsupervised learning techniques for real-world applications in computer vision, NLP, and robotics.

• Coursera
• 17 hours 31 minutes
• Self-Paced
• Paid Course

Machine Learning and Data Analytics Part 1

Dive into data mining fundamentals, exploring preprocessing, classification, clustering, and regression techniques through hands-on projects and real-world case studies.

• Coursera
• 15 hours 20 minutes
• Self-Paced
• Paid Course

Statistical Learning for Engineering Part 1

Explore supervised and unsupervised machine learning algorithms, from linear regression to SVMs, with hands-on Python implementation for real-world engineering applications.

• Coursera
• 1 day 11 hours 38 minutes
• Self-Paced
• Paid Course