Intermediate Reinforcement Learning

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

This article-based course explores the regulations, guidelines, and frameworks that govern the use of AI technologies across various sectors, addressing issues like data privacy and ethical considerations.

This course focuses on how AI can optimize demand forecasting, support risk mitigation strategies, and drive automation within supply chain operations through real-world examples and hands-on exercises.

This course equips learners with the strategies and tools to design, manage, and scale AI projects in real-world environments. It emphasizes applying agile methodologies and risk mitigation to optimize AI initiatives.

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 course equips you with the skills to analyze, implement, and assess large language models in real-world scenarios. You will learn about core LLM capabilities, summarization, translation, and how LL Ms power content generation. The course also covers building chatbots and sentiment analysis tools with LangChain and evaluating LLM performance using benchmarks like ROUGE, GLUE, and BIG-bench.

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 hands-on project-based course where you build a Support Vector Machine classifier using Scikit-Learn and the RBF Kernel to predict heart disease. It focuses on the practical implementation and evaluation of SV Ms.

This specialization focuses on applying NLP techniques to real-world problems. By the end of the series of courses, you will have hands-on experience in building NLP models for various tasks, including text summarization.

This course focuses on unsupervised learning, specifically clustering algorithms. It covers popular methods like K-Means and hierarchical clustering, and teaches how to apply them to real-world datasets.

This course teaches the fundamentals of statistical thinking using Python. You will learn to perform exploratory data analysis, think probabilistically, and understand the core concepts of statistical inference.

This course teaches you how to build predictive models using Scikit-Learn. You'll learn about classification and regression and apply your skills to real-world datasets.

This course introduces the fundamentals of reinforcement learning, guiding learners on how to frame RL problems and tackle classic examples. It covers basic algorithms and progresses to using function approximation with deep learning. It also features 'Project Malmo' for AI experimentation within Minecraft.

Reinforcement Learning: Complete Course with Python

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 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