An interactive explanation of how Ordinary Least Squares Regression works. This is widely used for predictive modeling in machine learning.

Part 2 of a visual introduction to machine learning covering model tuning and the bias-variance trade-off.

An interactive introduction to machine learning and decision trees which comes to life as you scroll.

Visual explanation of image kernels which are used in machine learning for 'feature extraction', a technique for determining the most important portions of an image.

Over-optimizing efficiency worsens the very goals it aims to achieve, known as Goodhart's law.

Learn how Stable Diffusion uses complex equations and machine learning to generate art from noise.

A visual and simplified exploration of the Transformer model in machine learning.

This post provides an intuitive introduction to the core concepts of deep learning. It covers the most important deep learning concepts and aims to provide an understanding of each concept rather than its mathematical and theoretical details. While the mathematical terminology is sometimes necessary and can further understanding, these posts use analogies and images whenever possible to provide easily digestible bits comprising an intuitive overview of the field of deep learning.

An introductory guide to LLMs captures the evolving ecosystem and practical application.

This article demystifies modern chatbots by dissecting how ChatGPT works.

An Iterative Process for Production-Ready Applications. Author Chip Huyen, co-founder of Claypot AI, considers each design decision - such as how to process and create training data, which features to use, how often to retrain models, and what to monitor - in the context of how it can help your system as a whole achieve its objectives.

Data Skeptic is your source for a perspective of scientific skepticism on topics in statistics, machine learning, big data, artificial intelligence, and data science. The weekly podcast and blog bring you stories and tutorials to help understand our data-driven world.

An exploration of the challenges in aligning artificial intelligence systems with human values and goals.

An exploration of the quest to find the “master algorithm” that can solve all machine learning problems.

A practical guide to implementing machine learning techniques using popular Python libraries.

A concise and accessible introduction to the core principles and algorithms of machine learning.

LSTMs are crucial for learning long-term dependencies in recurrent neural networks.

Explore the magical outputs and robustness of Recurrent Neural Networks in various data formats.

Annotated version of a transformer with comments adds explanation to the model architecture.

Kaggle is a popular online platform for data science and machine learning competitions, as well as a community for data enthusiasts, professionals, and researchers. It provides a wide range of datasets, notebooks, and public code to help users learn and develop their skills in data science and machine learning.

This AWS certification cover Fundamentals of AI and ML (20%), Fundamentals of Generative AI (24%), Applications of Foundation Models (28%), Guidelines for Responsible AI (14%), and Security, Compliance, and Governance for AI Solutions (14%).

This AWS certification covers Data Engineering (20%), Exploratory Data Analysis (24%), Modeling (36%), and Machine Learning Implementation and Operations (20%).

This modern and comprehensive Data Science and Machine Learning course on Udemy teaches you the latest skills using Python, TensorFlow 2.0, and other libraries. It covers everything from basic Python programming to advanced topics like Neural Networks, Deep Learning, and Transfer Learning. You'll build real-world projects, gain hands-on experience, and learn to work with popular tools such as Pandas, Scikit-learn, and Matplotlib. By the end, you'll have a portfolio of projects and the skills needed to become a professional Data Scientist, ready for the job market.

This book explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up-to-date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multi agent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.

A gentle introduction to Machine Learning. Linear Models and Logistic Regression are just the tips of the machine learning iceberg. There’s tons more to learn, and this playlist will help you trough it all, one step at a time.

Stephen Wolfram explores what's inside ChatGPT and its ability to produce meaningful text.

A non-technical course on AI technologies taught by Andrew Ng, one of the industry leaders in Artificial Intelligence. This course covers what AI is, building AI projects, AI in your company and AI and society.

A course on implementing Machine Learning in production taught by Andrew Ng, one of the industry leaders in Artificial Intelligence. This course covers the ML lifecycle and deployment, modelling challenges and strategies and data definition and baseline.

A course on specializing in Machine Learning taught by Andrew Ng, one of the industry leaders in Artificial Intelligence. This course covers supervised machine learning, advanced learning algorithms, unsupervised learning and recommender systems.