Beginner Reinforcement Learning

The original Stanford ML course taught by Andrew Ng

Comprehensive ML course covering regression, classification, clustering, deep learning, NLP, reinforcement learning.

This certification course provides policymakers, analysts, and public sector professionals with the knowledge to use AI responsibly. It covers governance frameworks, ethics, and real-world applications from predictive analytics to public safety.

This course covers several technologies on Google Cloud for data transformation including Big Query, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud.

This University of London course provides a practical introduction to the K-Means clustering algorithm, with a focus on the underlying statistical concepts.

A comprehensive introduction to the mathematical principles that form the foundation of artificial intelligence and machine learning, bridging essential concepts with real-world AI applications.

This course from the University of Colorado Boulder provides a modern take on regression analysis using the R programming language. You will learn about various regression techniques and how to apply them to real-world data.

A Duke University course that introduces the fundamentals of probability and data analysis using the R programming language, with a focus on real-world applications.

This course is designed for scientists, engineers, and other problem-solvers who want to learn the basics of statistical thinking and how to apply it to real-world problems. You will learn about data analysis, experimental design, and statistical modeling.

This comprehensive specialization covers the fundamental techniques of recommender systems, from non-personalized and content-based methods to collaborative filtering and advanced matrix factorization techniques. It is designed for both data mining experts and marketing professionals who want to gain a deeper understanding of these systems. The specialization includes a capstone project where you apply your knowledge to a real-world case study.

Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science. The course covers the basics of linear regression and its application in real-world scenarios.

Reinforcement Learning: Complete Course with Python

This course covers Support Vector Machines (SVM) from basic to advanced kernel-based models. It is designed for those who want to apply machine learning to real-world business problems and includes topics like hyperparameter tuning and model performance evaluation.

Learn Support Vector Machines in R Studio, from basic SVM models to advanced kernel-based SVM models. This course is for those who want to apply machine learning to real-world business problems using the R programming language.

This course focuses on building and deploying AI agents for various tasks. While not exclusively about document intelligence, several projects involve creating agents for research and data extraction, which are relevant to building OCR pipelines. It covers frameworks like CrewAI and Lang Graph.

A course focused on using AI for coding, specifically covering tools like Cursor and Claude Code for full-stack development and co-coding with AI agents.

This course focuses on creating and evaluating human data for SFT, RLHF, and red-teaming. It also covers mastering AI data quality and safety standards, which is a crucial aspect of data curation for AI models.

This course focuses on applying time series analysis to real-world business problems. You will work on projects like predicting temperature, COVID-19 cases, and stock prices.

University of Alberta Fundamentals of Reinforcement Learning