Machine Learning for Beginners - Data Scientists and Analysts
Comprehensive introduction to machine learning for data professionals, covering key concepts, techniques, and practical applications in business contexts, with hands-on coding examples.
From Classical Statistics to Modern Machine Learning
Explore the evolution from classical statistics to modern machine learning, examining key concepts like generalization, interpolation, and the "double descent" phenomenon in supervised learning.
Machine Learning Basics: A Speedrun - IPAM at UCLA
Speedrun through machine learning fundamentals, covering key concepts like inductive bias, regularization, and kernel methods for both linear and nonlinear regression.
Machine Learning and Neural Networks: Simple and Multiple Linear Regression - Day 5
Learn simple and multiple linear regression techniques for machine learning and neural networks, covering key concepts and practical applications.
Deep Learning Applications by Rina Panigrahy
Explore deep learning applications, from basic concepts to advanced techniques. Learn about neural networks, optimization methods, and real-world uses in image and speech recognition.
Detecting Cancer with RBF Networks Trained by Genetic Algorithms
Explore machine learning techniques for cancer detection using RBF networks and genetic algorithms, with practical insights on implementation and advanced concepts.
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.
LASSO in One Hour
Discover sparse modeling techniques for linear regression, PLS, and PCA with the LASSO method in this concise introduction.
Supervised Learning Algorithms - Lecture 3
Explore supervised learning algorithms, model evaluation, and Python implementation for classification and regression tasks in petroleum engineering and energy transition.
Amazing Algorithms for Solving Problems in Software
Explore bio-inspired algorithms like Firefly and Ant Colony for problem-solving in software. Learn their strengths, weaknesses, and optimal use cases to enhance your coding toolkit.
Using Machine Learning to Support Information Security
Explore machine learning applications in information security, covering types of ML, data gathering, and practical implementation strategies for InfoSec professionals.
Is It A Bug Or Is It A Story
Investigative data journalist shares insights on analyzing big data, focusing on outliers to uncover bugs or unique user behaviors, using examples from Iraq War Logs and FIFA Files.
Manipulating Machine Learning - Poisoning Attacks and Countermeasures for Regression Learning
Explore poisoning attacks on linear regression models, their impact on automated decisions, and effective countermeasures. Learn about optimization frameworks and statistical attacks in machine learning security.
Machine Learning Without the Hype
Explore AI, machine learning, and deep learning concepts, comparing rule-based and ML approaches for time-series data. Learn supervised vs unsupervised learning with practical examples and best practices.
The R Language - The Good, The Bad & The Ugly
Explores R's strengths and weaknesses as a data analysis language, discussing its unique features, statistical capabilities, and practical applications in research and bioinformatics.
Computers are Stupid - Protecting "AI" from Itself
Explore AI security, adversarial learning, and ethical challenges in machine learning. Learn about vulnerabilities, biases, and privacy risks in AI systems, and potential solutions for robust and secure neural networks.
Essentials of Math Modeling - Introduction to Regression and Statistical Ideas
Explore linear regression, data transformations, and statistical concepts in mathematical modeling. Learn MATLAB examples, sensitivity analysis, and problem-solving techniques for real-world applications.
Breaking Black-box AI
Explore failure cases of popular machine learning algorithms through live demos, from linear regression to deep learning, revealing limitations and potential pitfalls in AI applications.
Extending Scikit-Learn with Your Own Regressor
Learn to extend Scikit-Learn by implementing a robust Theil-Sen estimator, exploring its advantages over ordinary least squares and understanding Scikit-Learn's design and contribution process.
Machine Learning Under Test
Explore testing strategies for machine learning algorithms, focusing on code quality, domain-specific challenges, and practical implementation techniques for more robust and reliable ML models.
An Introduction to PyTorch & Autograd
Explore PyTorch's dynamic computation graphs and Autograd package for efficient deep learning, with practical examples and insights into GPU processing and algorithmic differentiation.