Alberta Machine Intelligence Institute Courses

Sample-based Learning Methods

44 reviews

Explore sample-based learning methods in reinforcement learning, including Monte Carlo, temporal difference, and Dyna. Learn to estimate value functions, implement algorithms, and improve sample efficiency.

• Coursera
• 22 hours 16 minutes
• On-Demand
• Paid Course

A Complete Reinforcement Learning System (Capstone)

27 reviews

Implement a complete reinforcement learning solution, from problem formulation to empirical study, developing skills to deploy RL in real-world scenarios.

• Coursera
• 15 hours 45 minutes
• On-Demand
• Paid Course

Fundamentals of Reinforcement Learning

25 reviews

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
• 15 hours 9 minutes
• On-Demand
• Paid Course

• Best of All Time

Prediction and Control with Function Approximation

23 reviews

Explore advanced reinforcement learning techniques for large state spaces, including function approximation, feature construction, and policy gradient methods. Apply these concepts to solve continuous-state control tasks.

• Coursera
• 21 hours 51 minutes
• On-Demand
• Paid Course

Introduction to Applied Machine Learning

747 ratings at Coursera

Comprehensive introduction to machine learning for professionals, covering problem definition, data preparation, and real-world applications across various domains. Develop skills to identify ML opportunities and translate business needs into ML solution…

• Coursera
• 6 hours 46 minutes
• On-Demand
• Paid Course

Machine Learning Algorithms: Supervised Learning Tip to Tail

417 ratings at Coursera

Explore supervised learning techniques like decision trees, k-NN, and SVMs. Implement and analyze these algorithms on real business cases, gaining practical skills in data preparation and model evaluation.

• Coursera
• 9 hours 24 minutes
• On-Demand
• Paid Course

Data for Machine Learning

98 ratings at Coursera

Develop skills to prepare, engineer, and validate data for machine learning models. Learn to identify biases, improve generality, and enhance model accuracy through thoughtful feature engineering.

• Coursera
• 11 hours 53 minutes
• On-Demand
• Paid Course

Optimizing Machine Learning Performance

51 ratings at Coursera

Synthesize applied ML knowledge to create a maintenance roadmap, analyze changing data, identify unintended effects, and operationalize models for confident project rollout and optimization.

• Coursera
• 11 hours 37 minutes
• On-Demand
• Paid Course

Microcredential

Generative AI Fundamentals

Dive into Generative AI fundamentals, from LLMs and Transformers to multimodal generation, building systems from scratch, and deploying responsibly with hands-on Python implementation.

• Coursera
• 8 weeks, 10 hours a week
• On-Demand
• Paid Course
• 3 courses

실용 머신 러닝 소개

머신 러닝의 실용적 적용을 위한 기초 과정. 비즈니스 문제 정의, 데이터 준비, ML 프로젝트 수행 방법을 학습하여 다양한 분야에서 ML을 효과적으로 활용할 수 있는 능력 배양.

• Coursera
• 6 hours 46 minutes
• On-Demand
• Paid Course

Microcredential

Generative AI Essentials

Discover how generative AI models work, master prompt engineering, and develop ethical frameworks for responsible AI deployment across text and image generation.

• Coursera
• 4 weeks, 10 hours a week
• On-Demand
• Paid Course
• 3 courses

Demystifying GenAI: Concepts and Applications

Explore GenAI's transformative power across industries through real-world case studies, ethical considerations, and future trends in this comprehensive foundation.

• Coursera
• 8 hours 33 minutes
• On-Demand
• Paid Course

Hands-On with GenAI Tools and Technologies

Explore GPT-4, DALL-E, and Stable Diffusion through hands-on practice, mastering configuration and creative applications for real-world problem-solving.

• Coursera
• 7 hours 14 minutes
• On-Demand
• Paid Course

Building and Deploying Generative AI Models

Master building, deploying, and scaling Transformer models, RAG systems, and autonomous AI agents from scratch using PyTorch, LangChain, and Google Cloud Platform.

• Coursera
• 13 hours 43 minutes
• On-Demand
• Paid Course

Ethics and Responsible Practices in GenAI

Explore ethical principles and responsible practices for generative AI, covering data privacy, bias mitigation, regulations, and societal impacts across various fields without requiring technical background.

• Coursera
• 12 hours 19 minutes
• On-Demand
• Paid Course