Alberta Machine Intelligence Institute
Public University • CA
Showing 11 courses from Alberta Machine Intelligence Institute
Demystifying GenAI: Concepts and Applications
Alberta Machine Intelligence Institute (via Coursera)
This course is designed for those curious about the transformative power of Generative AI across diverse sectors. It begins with an overview of GenAI's significance, exploring its core concepts and addressing common myths. Learners will delve into its applications in various industries, accompanied by real-world examples and case studies of successful projects. Through interactive learning and exploration, the course aims to provide a solid foundation for understanding and leveraging Generative AI's potential. By the end of this course, you’ll be able to: • Discuss generative AI's evolution and fundamental principles to distinguish its impact across various sectors. • Explore real-world applications of generative AI in various industries to recognize its transformative potential. • Examine case studies of generative AI projects, focusing on their strategies, outcomes, and societal impact. • Recognize generative AI's limitations and ethical considerations, classifying potential challenges and solutions.
Machine Learning Algorithms: Supervised Learning Tip to Tail
Alberta Machine Intelligence Institute (via Coursera)
This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
Hands-On with GenAI Tools and Technologies
Alberta Machine Intelligence Institute (via Coursera)
In this beginner level course on Generative AI, we take a closer look at popular tools and technologies such as GPT-4, DALL-E, and Stable Diffusion, alongside basic hands-on guidance for their configuration and use. This course aims to deepen understanding of each tool's unique capabilities and ease of usage. Learners will achieve proficiency in employing these tools for various creative tasks and will be able to compare GenAI solutions for solving real-world problems. By the end of this course, you’ll be able to: • Demonstrate basic proficiency in configuring and utilizing various generative AI tools. • Highlight features, capabilities, and use cases of different generative AI technologies, analyzing their effectiveness in specific contexts, using criteria such as ease of use, output quality, and versatility. • Synthesize best practices for integrating generative AI into workflows.
Generative AI for Audio and Images: Models and Applications
Alberta Machine Intelligence Institute (via Coursera)
Generative AI for Audio and Images: Models and Applications offers an in-depth exploration of how modern generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Transformers, and Diffusion models are used to create, manipulate, and enhance audio, image, and video content. Learners examine the architectures, training processes, and use cases of these models across different modalities, gaining both conceptual understanding and practical insights through hands-on activities. The course also highlights the ethical and societal implications of generative AI, including bias, transparency, intellectual property, and the challenges of deepfake technologies. By covering foundational theory as well as state-of-the-art approaches and applications, this course prepares learners to apply and develop generative AI creatively and responsibly for the audio and image modalities. By the end of this course, learners will be able to: Outline core concepts, challenges, and the history of AI-generated audio. Analyze important foundational audio generation models, such as variational and vector quantized autoencoders (VAE and VQ-VAE) Examine how these models integrate with the latest GenAI technologies to form hybrid, state-of-the-art transformer and diffusion-based audio generation systems, Study the architecture and functionality of Generative Adversarial Networks (GANs), and their variations. Implement and train GAN models for creating and enhancing visual content, Explore cutting-edge techniques such as diffusion models and transformers for image and video creation. Discuss the ethical considerations regarding generative AI for audio and images.
Ethics and Responsible Practices in GenAI
Alberta Machine Intelligence Institute (via Coursera)
Ethics and Responsible Practices in Generative AI is a self-paced course that helps you build a clear understanding of how to use generative AI in a thoughtful and responsible way. You do not need any technical background, just a curiosity about the role AI plays in our world and how to use it ethically. In less than 10 hours, you will do more than explore what generative AI is. You will learn how to think critically about its impact across different fields, including media, education, healthcare, and business. You will discover how to recognize ethical risks, such as bias or misuse of data, and how to apply practical strategies to address them. This course is designed to support both professionals and learners who want to use AI in ways that are both effective and responsible. You will also take a step back to consider the bigger questions. What does it mean to be human in a world where machines can create and decide? How is AI shaping our values, decisions, and society? Through reflection and real-world examples, you will explore how ethics and technology intersect in powerful ways. By the end of this course, you will be able to: • Explain the key ethical principles that guide the use of generative AI • Identify ways to manage data privacy, fairness, and other challenges in AI projects • Apply responsible AI practices using real case studies and examples • Reflect on how generative AI influences human values and social norms • Think ahead to the future of ethical challenges in AI This course will help you gain the confidence to engage with AI technologies in a way that is informed, respectful, and centered on human responsibility.
Data for Machine Learning
Alberta Machine Intelligence Institute (via Coursera)
This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
Introduction to Applied Machine Learning
Alberta Machine Intelligence Institute (via Coursera)
This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project. By the end of the course, you will be able to clearly define a machine learning problem using two approaches. You will learn to survey available data resources and identify potential ML applications. You will learn to take a business need and turn it into a machine learning application. You will prepare data for effective machine learning applications. This is the first course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.
실용 머신 러닝 소개
Alberta Machine Intelligence Institute (via Coursera)
이 강좌는 머신 러닝에 관심이 있으며 데이터 분석 및 자동화에 머신 러닝을 적용하길 원하는 전문가를 위한 강좌입니다. 이 강좌는 금융, 의약품, 공학, 비즈니스 등 분야와 상관없이 머신 러닝 프로젝트에서 문제를 정의하고 데이터를 준비하는 방법을 소개합니다.이 강좌를 수료하고 나면 머신 러닝 문제를 두 가지 접근 방법으로 정의할 수 있을 것입니다. 또한 이용 가능한 데이터 자료를 조사하고 잠재적 ML 적용을 알아보는 방법을 알게 될 것입니다. 비즈니스 니즈를 파악하고 실용 머신 러닝에 적용하는 방법을 알게 될 것입니다. 그리고 머신 러닝을 효과적으로 적용하기 위해 데이터를 준비할 수 있을 것입니다.이 강좌는 Coursera와 Alberta Machine Intelligence Institute에서 준비한 첫 번째 실용 머신 러닝 전문 과정입니다.
Optimizing Machine Learning Performance
Alberta Machine Intelligence Institute (via Coursera)
This course synthesizes everything your have learned in the applied machine learning specialization. You will now walk through a complete machine learning project to prepare a machine learning maintenance roadmap. You will understand and analyze how to deal with changing data. You will also be able to identify and interpret potential unintended effects in your project. You will understand and define procedures to operationalize and maintain your applied machine learning model. By the end of this course you will have all the tools and understanding you need to confidently roll out a machine learning project and prepare to optimize it in your business context. To be successful, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the final course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute (Amii).
Introduction to Generative AI: Concepts and Techniques
Alberta Machine Intelligence Institute (via Coursera)
This four-module course gives you a clear, practical foundation in Generative AI from what it is and where it’s used, to how modern models work and how to apply them responsibly. You’ll start with the big picture: GenAI capabilities across text, image, audio, and video, plus real-world industry applications. Then you’ll dive into the science behind today’s Large Language Models: text representation (tokenization, embeddings), and the Transformer architecture (positional encoding, self-attention, encoder/decoder flow). Next, you’ll get hands-on with LLMs and workflows: crafting effective prompts, calling models via web/UI and APIs, running models locally (e.g., via Ollama), and extending capabilities with Retrieval-Augmented Generation (RAG) and fine-tuning. Finally, you’ll examine challenges and responsible practice, including copyright, privacy and security, explainability, and questions of ownership in the GenAI era. Designed for learners with basic Machine Learning and Python familiarity, the course blends short lessons with labs, quizzes, and exercises. By the end, you’ll understand the core concepts and architectures behind GenAI with a strong sense in ethical and responsible use and GenAI limitations. By the end of this course, learners will be able to: Explain how generative AI spans text, image, audio, and video and assess real industry workflows where it creates value. Trace the evolution of language modeling from probabilistic/NLP approaches to Transformers, and justify why attention overcomes prior limitations. Understand tokenization and word embeddings, and reason about how these representations affect model behavior. Decompose a Transformer block and follow tensors, through self-attention, MLPs, and normalization to explain how representations are formed and refined. Operate LLMs via web UIs, APIs, and locally with Ollama to write minimal inference code and improve outputs using prompt patterns and get familiar with concepts of RAG and Fine-...
Building and Deploying Generative AI Models
Alberta Machine Intelligence Institute (via Coursera)
Transition from theoretical concepts to production-ready engineering in this hands-on course which is the final part in "Fundamentals of Generative AI" specialization. Designed for learners ready to move beyond the theory, this course focuses entirely on construction: you won't just learn about Large Language Models (LLMs); you will build, refine, and deploy them. We start at the foundational level, coding different types of Transformer architectures from scratch using PyTorch. Through high-performance training with Automatic Mixed Precision and ROUGE/BLEU evaluation, you will learn the techniques to scale custom components into optimized systems. By utilizing pre-trained models and weighing performance trade-offs, you will gain the insight needed to select the most efficient path for large-scale deployment. Moving to applied architecture, you will master Retrieval Augmented Generation (RAG) using LangChain, learning to evaluate pipelines and apply advanced techniques such as different chunking strategies, reranking and compression, and query transformation. You'll also navigate model selection as well as the critical trade-offs between RAG and Fine-tuning. Finally, you will step into the future of AI by developing autonomous Agents. You will bridge the gap between development and production by setting up a professional workflow with Poetry and deploying a Summarizer AI Agent directly to the Google Cloud Platform (Vertex AI). By the end of this course, you will possess a tangible portfolio of code and a live deployment, proving your ability to engineer robust Generative AI solutions.