Alberta Machine Intelligence Institute Courses

Sample-based Learning Methods

44 ratings

Learn near-optimal policies from trial-and-error experience: implement Monte Carlo prediction and control, TD learning, Expected Sarsa, Q-learning, and the Dyna model-based architecture.

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

A Complete Reinforcement Learning System (Capstone)

28 ratings

Build a complete reinforcement learning system: formalize a word problem as an MDP, implement an Expected Sarsa or Q-learning agent with neural networks and RMSProp, then run a parameter study.

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

Fundamentals of Reinforcement Learning

25 ratings

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts wi…

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

• Best of All Time

Prediction and Control with Function Approximation

24 ratings

Scale reinforcement learning to infinite state spaces: implement TD, expected Sarsa, Q-learning and Actor-Critic with tile-coding and neural network function approximation.

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

Career Certificate

Reinforcement Learning

3599 ratings at Coursera

Implement a complete reinforcement learning solution end to end, covering RL fundamentals, sample-based learning methods, and prediction and control with function approximation.

• Coursera
• 8 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 4 courses

Introduction to Applied Machine Learning

748 ratings at Coursera

Turn a business need into a machine learning problem: define ML questions, survey data sources, prepare training data, and apply the ML Process Lifecycle to a case study.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

Machine Learning Algorithms: Supervised Learning Tip to Tail

417 ratings at Coursera

Implement decision trees, k-NN, regression and support vector machines in Jupyter notebooks, then compare model performance and data preparation choices on business case studies.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

Data for Machine Learning

98 ratings at Coursera

Prepare data for machine learning success: source and clean scattered data, engineer features, spot bias and bad data, and counter overfitting with test and validation measures.

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

Optimizing Machine Learning Performance

54 ratings at Coursera

Walk a complete machine learning project into production: set ML business strategy, apply responsible ML frameworks, plan operational integration, and build a maintenance roadmap for changing data.

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

Career Certificate

Generative AI Essentials

11 ratings at Coursera

Learn how models like ChatGPT and DALL·E generate text and images, master prompt engineering, fine-tuning, RLHF and RAG, and deploy GenAI ethically under global regulations.

• Coursera
• 4 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 3 courses

Career Certificate

Generative AI Fundamentals

6 ratings at Coursera

Build generative AI end to end: tokenization, embeddings and Transformers, then VAEs, GANs and diffusion for audio and images, plus RAG and agentic systems with Google's ADK.

• Coursera
• 8 weeks, 10 hours a week
• Self-Paced
• Paid Course
• 3 courses

실용 머신 러닝 소개

비즈니스 요구를 정의하고 데이터를 준비해 실제 머신 러닝 프로젝트로 전환하는 실용적 입문 과정입니다.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

Building and Deploying Generative AI Models

Build Transformer architectures from scratch in PyTorch, engineer RAG pipelines with LangChain, and deploy a summarizer AI agent to Vertex AI with monitoring.

• Coursera
• 3 weeks, 5 hours a week
• Self-Paced
• Paid Course

Hands-On with GenAI Tools and Technologies

Configure and use generative AI tools like GPT-4, DALL-E, and Stable Diffusion to create text and images, compare their outputs, and apply GenAI project best practices.

• Coursera
• 4 weeks, 2 hours a week
• Self-Paced
• Paid Course

Ethics and Responsible Practices in GenAI

Learn to apply ethical frameworks to generative AI: address data privacy and bias, compare international AI regulations, and evaluate impacts on jobs, media, and education.

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